This project titled “BUS FITNESS PREDICTION using CNN [PBFUC]”, presented by Hirak Bose, Md Mosharof Hossen and Rakibul Hasan at the Department of Computer Science and Engineering, Daffodil International University, has been accepted as satisfactory for fulfilling the partial requirements. for the degree of B.Sc. Department of Computer Science and Engineering Faculty of Information Science and Technology Daffodil International University. We hereby declare that, this project was done by us under the supervision of Nusrat Jahan, Sr.
We also declare that neither this project nor any part of this project has been submitted elsewhere for the award of any degree or diploma. Deep knowledge and great interest from our supervisor in "Image Processing & Machine Learning" to carry out this project. Her endless patience, scholarly guidance, constant encouragement, constant and energetic supervision, constructive criticism, valuable advice, reading many inferior drafts and correcting them at every stage have made it possible to complete this project.
We would like to express our heartfelt thanks to Dr.Touhid Bhuiyan, Professor, and Head, Department of CSE, for his kind help to complete our project and also to other faculty member and the staff of CSE department of Daffodil International University. We would like to thank our entire coursemate in Daffodil International University who participated in this discussion while completing the coursework. In this paper, we proposed a model of Convolutional Neural Network (CNN) to classify fitness of buses.
In this system we have worked on 2 classes to classify bus fitness - one class is fit buses and another one is unfit buses.
Introduction
- Motivation
- Rational of the Study
- Research Questions
- Expected Outcome
- Report Layout
With the rapid growth of new technology, many innovative ideas have come forward to reduce traffic accidents and strict traffic rules. For that reason, many researchers have worked to reduce traffic accidents using image processing technology. The main result of this paper is classification of bus suitability to reduce the danger of traffic accidents in the whole country.
We worked on a system with a CNN-like algorithm which helped to perform a prominent proposal for bus fitness classification. For this, a crucial issue of our country like road accidents that take many lives every year will decrease. In our proposal, we tried to assemble the security camera and the bus to classify the bus as suitable or unsuitable.
So we decided to work on the suitability of the buses, which is another cause of road accidents. Gradually, we got some ideas for classification methods to classify the suitability of buses from that using large amount of image data sets. The traffic authorities will easily detect the suitability of the buses which they can check and take appropriate measures.
If the traffic authority installs our system on the roads, there will be some costs for using the security camera, PC, maintenance, etc. The first chapter highlighted many things like introduction, motivation, several questions arising from our research, expected result of this research and the last thing is the whole format of this report. The second chapter shows the comparative analysis, the many opportunities of our research and the exceptions of our contribution.
Show requirements of this kind of work, way of data collection, illustrated how to work this system and statistical part of our system. The main purpose of this system is to illustrate how good or bad our system is for the environment and society. And last one is the chapter six, which provided the core of the total thesis and final speech and future works scope of our study.
Background
- Terminologies
- Related Works
- Comparative Analysis & Summary
- Scope of the problem
- Challenges
In the same way that human minds fill in the gaps, neural frameworks do the same. The last fully connected layer allows us to vote on the classes we are interested in. This approach is done until we have a well-defined neural network trained with weights and feature detectors.
There was no work on this topic, so we can say that there is a lot of room to update this system. This system will track fitness of buses and traffic authorities will be benefited by this system to check everything from internal and external parts of buses. Basically we focused on the external part of buses to detect fitness which is external appearance.
Researchers can therefore make this system more effective by combining internal and external parts of buses in the future. After training all the data with numerous layers with different epochs it took so long in our machine. There was no other data set or other research related to our study, so it was difficult to manage it and move forward to achieve the highest level of accuracy in our results.
Research Methodology
- Research Subject & Instrumentation
- Data Collection and Procedure
- Statistical Analysis
- Proposed Methodology
- Experimental Setup
- Experimental Result & Analysis
After dividing the data into two classes as a suitable guide and an unsuitable guide, we decided to expand the data. The dataset was then split into training and testing parts after being classified into classes. We used 80% training data and 20% testing data to generate the CNN.
One of the famous artificial fields is Computer Vision, which is developing day by day for shaking deep learning. Max polling, one of the types of voting layer, contains the largest element of corrected feature map, which is used in this paper. Edges, corners, feathers are commonly used by this layer to identify parts of the images.
Where VGG16 is used and 16 layers are given, a normal CNN model must take into account the number of layers, padding and other factors. Other two classification models, such as the pre-trained VGG16 and another CNN classifier, did not provide good accuracy. VGG16 takes much longer to run than our home-built CNN model that we initially built.
So there is a lot of positivity for the first CNN classifier built, as long as the highest test accuracy is present. Our second CNN classification model does not have good accuracy testing and training, which missed the important part of a classification system. Where we split our total data into 80% for training and 20% testing data for our full model.
Through Figures 4.2.2 and 4.2.3, we illustrated the highest accuracy of validation and training and the loss of training and validation that we found in the first CNN classification model. Daffodil International University 21 For CNN, we achieved an accuracy of 80.93% and split our total data as 20% test data.
Impact on Society, Environment and Sustainability
Impact on Society
Ethical Aspects
Sustainability Plan
Conclusion, Recommendation and Future Works
Conclusions
Implication for Further Study
11] Jabbar, Rateb, Mohammed Shinoy, Mohamed Kharbeche, Khalifa Al-Khalifa, Moez Krichen, and Kamel Barkaoui, "A Driver Drowsiness Detection Model Using Convolutional Neural Network Techniques for Android Application," IEEE. 12] Benjdira, Bilel, Taha Khursheed, Anis Koubaa, Adel Ammar, and Kais Ouni, "Car detection using unmanned aerial vehicles: Comparison between faster r-cnn and yolov3,". 13] Şentaş, Ali, İsabek Tashiev, Fatmanur Küçükayvaz, Seda Kul, Süleyman Eken, Ahmet Sayar and Yaşar Becerikli, "Performance evaluation of support vector machine and convolutional neural network algorithms in real-time vehicle type and color classification".
15] Bhaskar, Prem Kumar and Suet-Peng Yong, “Image Processing Based Vehicle Detection and Tracking Method”, IEEE. 16] Koga, Yohei, Hiroyuki Miyazaki and Ryosuke Shibasaki, “A CNN-based method for vehicle detection from aerial images using hard sample mining,” Remote Sensing. Branch, “Detection and Classification of Motorcycles in Urban Scenarios Using a Model Based on Faster R-CNN,” ICPRS.
First one is to decide on the methodological way to complete our work and second one is to collect data. So, we are determined that our system "Predicting Bus Fitness Using CNN (PBFUC)" will create a positive and effective atmosphere on users to make safe road.
