Real Time Object Detection using Machine Learning

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Real Time Object Detection using Machine Learning

BY

MD. Moshiur Rahman ID: 172-15-9797

Shajeeb Chakma ID: 172-15-9657

This Report Presented in Partial Fulfillment of the Requirements for The Degree of Bachelor of Science in Computer Science and Engineering

Supervised By Abdus Sattar Assistant Professor Department of CSE Daffodil International University

DAFFODIL INTERNATIONAL UNIVERSITY

DHAKA, BANGLADESH MAY, 2021

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APPROVAL

This Project/Internship titled “Real-time Object Detection using Machine Learning”, submitted by Md. Moshiur Rahman, and Shajeeb Chakma to the Department of Computer Science and Engineering, Daffodil International University, has been accepted as satisfactory for the partial fulfillment of the requirements for the degree of B.Sc. in Computer Science and Engineering (BSc) and approved as to its style and contents. The presentation has been held on April 2021.

BOARD OF EXAMINERS

_______

_________________

Dr. Touhid Bhuiyan Professor and Head

Department of Computer Science and Engineering Faculty of Science & Information Technology Daffodil International University

Chairman

_____________

Md. Sadekur Rahman Assistant Professor

Internal Examiner

________________

Dr. Fizar Ahmed Assistant Professor

Internal Examiner

________________________

Dr. Shamim H Ripon Professor

Department of Computer Science and Engineering East West University

External Examiner

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DECLARATION

We now declare that we have done this thesis under the supervision of Abdus Sattar, Assistant Professor, Department of CSE Daffodil International University. We declare this thesis nor is any part of this thesis submitted elsewhere to award any degree or diploma.

Supervised by:

Abdus Sattar Assistant Professor Department of CSE

Daffodil International University

Submitted by:

Md. Moshiur Rahman ID: 172-15-9797

Department of CSE

Shajeeb Chakma ID: 172-15-9657 Department of CSE

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ACKNOWLEDGEMENT

First, we express our heartiest thanks and gratefulness to Almighty God for His divine blessing makes us possible to consummate the final thesis prosperously.

We grateful and wish our intense responsibility to Abdus Sattar, Assistant Professor, Department of CSE Daffodil International University, Dhaka. Deep Knowledge & keen interest of our supervisor in “Real-time object detection” to carry out this thesis. His endless patience, scholarly guidance, continual encouragement, constant and energetic supervision, constructive criticism, valuable advice, reading many inferior drafts, and correcting them at all stages have made it possible to complete this thesis.

We want to express our heartiest gratitude to Pro. Dr. Akhter Hossain, Professor, and Head of, Department of CSE, for his kind help finish our thesis and other faculty members and the staff of the CSE department of Daffodil International University.

We want to thank our entire course mate in Daffodil International University, who took part in this discussion while completing the course work.

Finally, we must acknowledge with due respect the constant support and passion of our parents.

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ABSTRACT

Real-time-object-detection is object detection in authentic time with expeditious inference while maintaining a base level of precision. It is a cosmic, energetic yet uncertain and complicated space of PC vision. Because of its increased use in reconnaissance, the global positioning framework used in security and numerous other applications has moved scientists to never-ending device more severe and proficient calculations. In any case, difficulties arise in executing object identification and following in actual time, like following under robust climate, an excessive calculation to fit the accurate time execution, or multi-camera multi- objects following make this assignment exhaustingly burdensome. Item identification is a PC vision procedure that sanctions us to distinguish and find objects in a picture or video.

Article identification sanctions us to immediately consign the sorts of things found while finding occasions of them inside the picture. However, numerous strategies and procedures have been grown, yet in this writing audit, we have examined some celebrated and straightforward techniques for object recognition and following. In the end, we have withal given their overall applications and results.

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TABLE OF CONTENTS CONTENTS

PAGE

Board of examiners i

Declaration ii

Acknowledgements iii

Abstract iv

CHAPTER

CHAPTER 1: INTRODUCTION 1-3

1.1 Introduction 1.2 Motivation

1.3 Research Question 1.4 Research Methodology 1.5 Research Objective 1.6 Report Layout

01 01 02 02 03 03 03

CHAPTER 2: BACKGROUND 4-5

2.1 Introduction 04

2.2 Related work 04

2.3 Bangladesh Perspective 05

CHAPTER 3: RESEARCH METHODOLOGY 6-13

3.1 Introduction 06

3.2 Test Data Set 07

3.3 Data Pre-Processing 7-8

3.4 Structure of the Model 8-10

3.5 Learning rate and Optimizer of the model 10-11

3.6 Data Augmentation 11

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3.7 Training the model 12-13

CHAPTER 4: PERFORMANCE OF THE PROPOSED MODEL 12-14

4.1 Object Recognition API 12

4.2 Depth Wise Separable Convolution 13

4.3Model efficiency 14

CHAPTER 5: RESULT COMPARISON AND DISCUSSION 15

5.1 Image classified 15

CHAPTER 6: ASSUMPTION AND FUTURE WORK 16

REFERENCES 17

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LIST OF FIGURES

FIGURES PAGE NO.

Figure 3.1.1 Steps of Data Collecting and Processing 06

Figure 3.2.1 Collected Dataset 07

Figure 3.4.1 Representation of model summary 10

Figure 3.7.1 Convolutional neural network layer 12

Figure 4.1.1 Validation data 14

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CHAPTER 1 INTRODUCTION 1.1 Introduction

Object recognition describes an amassment of similar machine image detection that include actions like classifying objects in digital images. Image relegation includes projects such as forecasting the subject of one thing in an image. Object localization leads to recognizing one or more portions in an image and moving flowing box around their area. Object detection does the work of combines these two tasks and surrounds and transfers one or more objectives in an image. When a utilizer or practitioner points to the session object realization, they often expect object detection. It may be exciting for abecedarians to recognize between another like machine vision tasks. Concept relegation involves allowing a class name to model, whereas object localization requires moving a bounding box throughout one or more objectives in an image. Object detection is forever more challenging and cumulates these two tasks and moves a covered box about same object of engagement in the image and allows them a class label. Commonly, all these puzzles are connected to as object apperception.

1.2 Motivation

Object identification is connected to other fellow PC vision procedures like picture detection and picture division. It helps us to understand and analyze pictures in pictures or video. Item location is happening into an enormous scope of experiments, with use cases working from personal defense to profitability in the work conditions. Facial identification can be a security application to give specific people a mainly consigned space of a system working. It is accustomed to collect the number of individuals now inside an organization to develop other functional carriers so that the time used for that particular party will be smoother.

In addition, it is used in visual Internet searchers to enable profit-seekers to track solid objects.

The pursuit of the Internet is an example because the social and shopping phases revolve around this innovation. These highlights use people and item classifications to stimulate large amounts of information for various purposes in working condition

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1.3 Research Question:

Here is all question of following topics:

• What is object detection used for?

• What potential does it have?

• How is it currently being used?

• How real time object detection be improved?

• Which model is best for object detection?

1.4 Research Methodology

Here is the methodology for this research:

• Deep learning algorithm for image classification. (using SSD Mobile Net V3).

• Model for Machine learning (Tensor flow frozen model).

• Using Python Language & open CV.

1.5 Research Objectives

There are some benefits of using real time object detection. Some objectives are given below:

• Detect all object such as people, car, bicycle, truck etc.

• Children can learn unknown objects name.

• Reduce car accidents.

• In astronomically immense city its avail to minimize traffic jam.

• Single image detect.

• Mp4 video detect.

• Live web cam.

1.6 Research Plan

Chapter 1: Will discuss about summary, Motivation, Research Question, Explore Methodology and the predictable result of our project.

Chapter 2: Will discuss about Bangladesh perspective and traffic system in road transportation.

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Chapter 3: It is focus to the result and benefit of using real time object detection.

Chapter 4: It describes the conclusion of this research.

Chapter 5: Here all the references we used for this research.

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CHAPTER 2 BACKGROUND 2.1 Introduction

Real-time object detection aims to identify all cases items from a kenned period, like individuals, vehicles, or appearances in a picture. By and large, just a microscopic number of cases of the article are available in the picture, yet there is a significantly and massively enormous number of potential areas and scales at which they can happen and need to, by one way or another, investigated. Every identification of the picture is an account with some posture data. It is a primary area of the item, an area, scale, or the degree of the article characterized by a bounding box.

2.2 Related Works

Analysts have created an assortment of techniques for identifying deterrents in vehicles. Takeda proposed an infrared recognition unit for showing beams interfering with vehicle objects.

Specialists have used different radar frameworks for identifying vehicles at the intersection limits.

We proposed various kinds of optical gadgets to recognize crossing rail line impediments. One proposed an alert framework utilizing a worldwide situating framework to identify vehicle objects.

Zhang used a Programmable Logic Controller (PLC) to plan a keen caution framework for notice vehicle drivers of passing articles. With the improvement of PC capacities, strategies dependent on picture handling have been utilized to identify vehicle objects. Contrasted and the prior techniques, the strategies dependent on picture preparation are more natural, more affordable, and more precise. We mounted camcorders on the facade of vehicles to identify potential deterrents utilizing the camera-movement assessment strategy. González and Wei utilized foundation deduction procedures to distinguish objects in a railroad climate. Built-up a high-level wellbeing framework is dependent proceeding mechanism image to recognize affecting hindrances by a rail route adventure. Teng et al. utilized a super-pixel-based rail line object recognition calculation and a help vector machine to group deterrents. The previously mentioned strategies, which depend on picture handling or customary AI procedures, were assessed on minor product by different impediments the foundation with little variety, lighting with little change, and restricted item extent. Thus, these techniques can barely fulfill the necessities of proper rail line object

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recognition. Nonetheless, the accomplishment of profound learning prompted forward leaps in the field of item identification. A few article discovery strategies [1, 2, 9, 10, 26, and 27] have been proposed as of late. Generally, a couple of deep convolution neural organizations utilizations to rail line object discovery have accounted. Guo suggested a rail line passerby recognition method that combined an SSD flexible Net with an Image is Transformed Gradient, resulting in high precision and consistency. However, the calculation cannot complete the preparation and position from start to finish, and the exploration execution for low- and mid and micro objects should be given further thought.

2.3 Bangladesh Perspective

In Bangladesh's viewpoint, the incredible danger of expanding street auto collisions is a problematic issue. Because of how street auto collisions lead to countless deadly debilitating wounds, the results of these mishaps are essentially reflected in the social circle. Decreasing street mishaps is genuinely supportive of utilizing constant article object detection. For a modern reason, it is accustomed to collect the number of individuals now inside an organization to develop other functional carriers so that the time used for that particular party will be smoother. For visually impaired and chemical imbalance kid's continuous item discovery precious for them to gain some new proper knowledge.

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CHAPTER 3 RESEARCH METHODOLOGY 3.1 Introduction

Figure-1. Collecting and Processing

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3.2 Test Data Set

There are six categories in this sample, which contains over 1400 square images. The bulk of the photographs in this sample were captured in areas and on roads, while some were downloaded from the internet. The aggressive aspects were really that manually sorting all of the details from the region was difficult due to the sporadic problem. This dataset was created to facilitate an image postulation. There are approximately 32 images, all of which are for the training model in a dataset, and the rest are for testing purposes. There are six types of train and test informational sets in the exam. Approximately 200 to 250 photos are separated for preparation, and approximately 32 images are chosen for each class's research.

Figure 2. Collected Dataset

3.3 Data Pre-Processing

The entirety of information has been gathered from Google and some physically from the field by cell phone pictures were not in a similar size and goal It was very difficult to prepare and validate the data while attempting to use it. This is an adaptation of a datasets with a fixed image plan. We converted total images into square measurements in accordance with our task requirements. We got the images to a set-up target of 256 * 256, which is the image's down-tested size. At that point, we scaled it into the necessary measurement structure of the trimmed images. We use level mg to preprocess every one of the pictures. Also, we prepared our design on RGB color mode.

3.4 Structure of the Model

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resemblance, a few dense layers, and failures is included to aid in the use of the model. The first linear function is classified as an information layer, with a piece size of 2 and an input shape of 42 x 42 in LED shading mode, as well as 64 channels. The actuation surface has a similar cushioning property as the level with moves. When the REL receives constructive input value, it returns the worth, and when it receives uninterested input worth, it returns zero back.

T(X) = MAX (1,0 ,x) (1)

The primary max-pooling layer is augmented with the permutation model yield. To create binned top speed sub-districts, reduce the number of frames and add pool size 2 to the top pooling layer with steps 2 and 3. The amount of strain is 32 in the next step, with a part size of 2 and a move of 0. This process contains community similarity to regularize the convolutional network, which concentrates slope during planning and can increase execution and reduce preparation time. The use of group uniformity here keeps the schooling period active and allows for a higher learning pace. Permutation stage aftereffect is contrasted to the input image that follow. The layer has furthermore the same pool size 2 with steps 2.

Frame relay has huge channel sizes, but it shares the same properties as protocol stack in terms of segment size, walks, and bunch uniformity. Frame relay produces yield, which is represented to the third pooling layer, with a pool size of two stages. Control plane incorporates a similar feature to tier 4 and has a segment of 144. Regardless, a 45-percentage dropout is used in this surface to make the shape more stable during training, and may be highly solid or normalized.

Four large pieces write a network vector raise after these 4 levels. With enactment esteem, the big central layer has 256 secret components, and the dropout rate is 35%. This method is used to deal with the over-fitting dropout process. The thickness of the next large piece is 5 through slacker to control the model, and the enactment is sigmoid. This is how the model is made.

By applying this surface to the model, outcome is turning out to be bland and loud free and afterward adding two completely associated thick layers for improving a presentation of the characterization task where there is likewise cluster standardization for drop thrown out property. The details would then be converted to chances for each class by adding a feature map to it.

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Two-fold cross assists with characterizing the misfortune, which goes about as a misfortune work for double characterization issues. From that point onward, the streamlining agent will assist with performing angle plunge, and the measurements set precision since this is an order issue.

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Figure: 3 model of summary

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3.5 Optimize the model

In light of preparing information, Adam Optimizer 0.002 updates network loads and results more active in the underlying training level. They refreshed the organization loads bypassing their specialized expressions, starting with one emphasis then onto the next cycle [12]. Loads are refresh by k (3), where k is the emphasis file:

wlij(k+l) = wlij(k) - µglij{k}l= 1,2 (3)

Having rate functions work well for creature 1cm to 10m design scales, which means that it's not based on the original scale. The model's most revolutionary extreme border is maintaining the training rate. The model of each update's opinion with information scale decays as follows:

Initial rate × {1/(1 + rot × cycle)} (4)

Used a small learning rate reduction and spinning down the schooling rate reduces the error rapidly. We were able to reclaim the right to name the training pace, that Kera's maintains. The feedback work is intended to tweak the model load in order to reduce the training intensity as the impact strength.

3.6 Data Augmentation

Developers turned 40 lines and the expanse, heights shifting series 0.4, for which magnification range is equal, to increase our train and study data. Changing the instruction data to improve the classifier's capacity and robustness with the aid of data increase will produce for each case. The method generates a pledge for each class as well as the selected class mark.

Data production has played a critical role in obtaining recent most surprising outcomes in many pest exposures tasks, and it is used to improve generalization points in this writing. Rescaling the image allows for parallel switching, illumination shifts, and casual RGB color. We must first produce the details before doing any further processing; this is referred to as modifying performance. The mirror's color mode is LED, and the LED coefficients vary from 25 to 355, but in this location, certain conditions will make it difficult to prepare the model.

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3.7 Training the model

When we train the model with our one-of-a-kind creepy-crawly informational indexes that offer us a superior presentation, we use four convolution operation and two completely related layers.

For 50 years, a clump size of 20 has been used. Our model achieves radiant fineness accuracy, with 86.37 percent planning exactness and 93.46 percent evaluation exactness. The picture's objective size is 42 x 42 for both training and testing. The Proposed method used here will describe the image to the point where all levels of different classes can accurately predict the image's type.

Figure: 4 Image layer

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CHAPTER 4 PERFORMANCE OF THE PROPOSED MODEL

4.1 Object Recognition API:

Receiver recognition is a frame constructed on top of OpenCV that makes it simple to practice and expand different object models. It's a tool for object disclosure that delivers AI and performs calculations. We used the Machine learning API for object position to allow this item recognition and follow through. Developing a learning model that can restrict and properly identify various articles in a solitary edge is a difficult task for computer visualization. TensorFlow was used to test a wide variety of heterogeneous architectures, ranging from cells and medications to significant distributed networks with a few computers and a large number of computing devices, such as GPU cards. The framework is adaptable, allowing it to convey a wide range of equations, including the creation and estimation of profound neural organization models.

4.2 Depth Wise Separable Convolution:

Portable Nets is built on the depth-aware distinct difficulty path and employs an initiation classical to decrease the computational cost of sheets, as seen in Figure 1. (a) Because of two factors, the normalized convolution strategy has been changed by the Depth-wise methodology two step convolution. 1.inside and out astute method; 2. spatial convolution conducted autonomously over each channel of input.

Employee for projecting channel data from profundity informative into various sound seasons is an important layer for convolution. Since DSC layers have less boundaries than traditional convolution layers, they need less operation to record. Thus, it is less expensive and quicker.

(E1 E2) (K x K) (1)

(K K DADA E1 E2) is the cost of computing it (2)

(K K E1 +1 1 E1 E2) Boundary size of Depth-wise detachable: (3) It has a computational cost of (K K DA DA E1 + 1 1 E1 E2) (4)

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The cost of computation is reduced as follows:

(KKDADAE1+E1E2DBDB)/(KKDADAE1E2DB) = (1/B) + (1/K2) (5) The diminished boundary is given as:

(KKE1+111E1E2)/(KKE1E2) = (1/E2) +(1/K2) along these lines (6)

4.3 Model efficiency

Our network model exists in an entity made up of various Differential scanning components. R e LU, cluster standardization, profundity shrewd, and fact - intelligent actions are the surfaces in the DSC element. The module's first surface is regular convolution, while the output unit is average pooling, which helps reduce the spatial target to 1. The conceptual framework is similar to the VGG organization, which eliminates the use of residual associations for faster computation. The standard layer receives 85 percent of Portable Net's computation power.

We created a Digital Net version called Cell Net, which combines the Smartphone Net classifier and the Single Shot Multi-Box Detector structure. We've put this unified type together to see what the benefits of using this consolidated form are and to compare it to other situation models. Since the Hard disk component is so large and far away from the rest of the mission, we'll provide a quick overview of how it works before getting into the details. Sd card, for the most part, uses different element layers as classifiers, with a slew of different perspective proportions serving as default boxes at each site. To evaluate each component chart, use a convolutional method.

Similarly, any classified correctly the packages' class scores and form counterbalance scores. The accuracy in anticipating the default confines is taken into account only if its Jaccard cover with the grid cell has an edge score of more than half at the time of preparation. The residual scores that do not fall within the expected category are then processed using sure and confinement ratings. The Mobile-Net configuration, which shares the same system architecture as the Hard disk. We are, however, using Mobile Net as a foundation instead of VGG in our work. In our methods, we have reintroduced a depth-aware divisible convolution approach in place of traditional convolution.

Finally, it is obvious that using SSD structure rather than depending on reference outline is superior

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fundamental level as well. However, the primary issue in this model is that it becomes delayed as more convolutions are incorporated.

Figure: 5 Model efficiency

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CHAPTER 5 RESULT COMPARISON AND ANALYSIS

Object detection is machine learning related to computer optics and image processing that recognizes and describes objectives such as person train, truck, boat, traffic light, fire hydrant, stop sign, parking meter, bench, the dog from digital images and videos. This technology has the capability to classify just one or various things within a digital vision at once. Object detection has been around for years but is becoming clearer beyond several manufacturers now more than ever before. The future of object detection has the extensive potential over a wide area of industries.

We are excited to be one of the main operators behind real-time object detection, high-performance camera, artificial intelligence, and machine learning, which has permitted us to create a solution that will never collapse video, allowing for various AI abilities which other companies cannot enable.

Using the frozen model, this section predicted our outcome of how much percentage recognized correctly predicted rate for each qualified and checked piece. The x-axis in the graph above represents things such as a human or a vehicle, while the y-axis represents the intensity of detection, which ranges from 0-1. The final output screen by using the Cell Net and Processor types. However, it takes approximately 3 seconds to detect a particular image. The subjects must also be at least 20 meters away from the web camera. Due to the extreme limited color calibrator pixel power of 2.5mp, the tracking rate decreases as the object moves away from that distance.

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CHAPTER 6 ASSUMPTION AND FUTURE WORK

We tried to comprehend the item in front of a web camera. The created model was tried and prepared to utilize TensorFlow Object Detection API, an edge works which Google made.

Perusing an edge from a web camera creates arguments, so there is a condition for significant improvements with each method to lessen Input/output issues. Thus, we zeroed in on the stringing technique, which improves outlines each next so that preparing period for everything is incredibly developed. The applying effectively recognizes each article before the webcam, and it requires almost 2-7seconds to move the item identified box over the next thing around there. By putting forward this initiative, we will be able to recognize and obey the article in the sports arena, causing the PC to respond deeply, which is, in effect, the use of Neural Networks. We will search the Traffic Signals by identifying the hospital vehicle in rush hour gridlock using the Public Surveillance Camera. This framework utilizes YOLO as the apparatus for object location in pictures prepared on the COCO dataset. Considers the location of explicit articles, like jar or toaster oven. An intriguing way to deal with this piece of the task is either to utilize other datasets or reevaluate the item identification approach in pictures. For example, one could carry out the semantic division and, in this manner, characterize all cells in the inhabitance framework inside the camera's field of view. By then, marking the cells as either destructive or innocuous, direction assessments utilize just the pictures. If this somehow happened to be joined with a self-restriction calculation, memory of the cells outside the camera's field of view could put away. In this sense, a considerably further developed matrix accomplished containing more data.

We could also detect corruption in public places by observing people's annoying quirks. Indeed, we will use this initiative on the road to prevent terrorist attacks by monitoring their movements along Bangladesh's border. In this way, the process of recognizing and following objects will be valuable, and the truth will be more accurate.

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REFERENCE

[1]. Ye, T., Zhang, Z., Zhang, X., & Zhou, F. (2020). Autonomous Railway Traffic Object Detection Using Feature- Enhanced Single-Shot Detector. IEEE Access, 1–

[2]. Kanimozhi, S., Gayathri, G., & Mala, T. (2019). Multiple Real-time object identification using Single shot Multi-Box detection. 2019 International Conference on Computational Intelligence in Data Science (ICCIDS). doi:10.1109/iccids.2019.8862041

[3]. Geiger, P. Lenz, C. Stiller, and R. Urtasun. “Vision meets robotics: The kitti dataset”. International Journal of RoboticsResearch (IJRR), 2013.

[4]. T. D. R. Girshick, J. Donahue and J. Malik. “Rich feature hierarchies for accurate object detection and semantic segmentation”. Computer Vision and Pattern Recognition (CVPR), 2014, 2014.

[5]. S. Ren, K. He, R. Girshick, and J. Sun. “Faster r-cnn: Towards real-time object detection with region proposal networks”. In Advances in neural information processing systems, pages 91–99, 2015.

[6]. M. St-Pierre and D. Gingras, “Comparison between the unscented kalman filter and the extended kalman filter for the position estimation module of an integrated navigation information system,” in IEEE Intelligent Vehicles Symposium. Citeseer, 2004, pp. 831–835.

[7]. E. A. Wan and R. Van Der Merwe, “The unscented kalman filter for nonlinear estimation,” in Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000. AS-SPCC. The IEEE 2000. Ieee, 2000, pp. 153–158.

[8]. D. Galar and U. Kumar, EMaintenance: Essential Electronic Tools for Efficiency. Academic Press, 2017. [9].

B. Khaleghi, A. Khamis, F. O. Karray, and S. N. Razavi, “Multisensor data fusion: A review of the state-of- the-art,” Information fusion, vol. 14, no. 1, pp. 28–44, 2013.

[10]. M. Haberjahn and K. Kozempel, “Multi level fusion of competitive sensors for automotive environment perception,” in Proceedings of the 16th International Conference on Information Fusion. IEEE, 2013, pp.

397–403.

[11]. F. Homm, N. Kaempchen, J. Ota, and D. Burschka, “Efficient occupancy grid computation on the gpu with lidar and radar for road boundary detection,” in Intelligent Vehicles Symposium (IV), 2010 IEEE. IEEE, 2010, pp. 1006–1013.

[12]. Tchamova and J. Dezert, “On the behavior of dempster’s rule of combination and the foundations of dempster- shafer theory,” in 2012 6th IEEE International Conference Intelligent Systems. IEEE, 2012, pp. 108–113.

[13]. Fernandez, “Grid-based multi-sensor fusion for on-road obstacle detection: Application to autonomous driving,” Ph.D. dissertation, Master’s thesis, KTH Royal Institute of Technology, Stockholm, 2015.

[14]. “YOLO: Real-Time Object Detection,” https://pjreddie.com/darknet/yolo/, accessed: 2019-03-24.

[15]. “YOLOv3 in Pytorch,” https://github.com/ultralytics/yolov3, accessed: 2019-03-02.

[16]. H. Moravec and A. Elfes, “High resolution maps from wide angle sonar,” in Proceedings. 1985 IEEE international conference on robotics and automation, vol. 2. IEEE, 1985, pp. 116–121.

[17]. C. Coué, C. Pradalier, C. Laugier, T. Fraichard, and P. Bessière, “Bayesian occupancy filtering for multitarget tracking: an automotive application,” The International Journal of Robotics Research, vol. 25, no. 1, pp. 19–

30, 2006.

[18]. T. Gindele, S. Brechtel, J. Schroder, and R. Dillmann, “Bayesian occupancy grid filter for dynamic environments using prior map knowledge,” in 2009 IEEE Intelligent Vehicles Symposium. IEEE, 2009, pp.

669–676.

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Plagiarism Checked by

Abdus Sattar, Assistant Professor, Department of CSE

02-05-2021