Metrics of YOLOv5 Empty Shelf Training
8.2 Recommendations for Future Work
The limitations of ED-App should be improved for a better work and implementation in the future and real-world situations. Table below listed the
recommendations based on the limitations of this application for future enhancement.
Table 8.1: Limitations and recommendations for the ED-App.
No. Limitation Reason Recommendation 1 The application
does not produce a smooth preview video of the surveillance tool.
The handshake between Telegram and the backend system has 3-5 seconds delay that will exceed the exhaust time.
Improve the integration between Telegram and backend system with higher GPU or better API resources.
2 The application detected all black, long items as empty shelf.
The accuracy of the trained model is not high enough due to small size of dataset.
Include more data in the training, e.g., 500 images for training and 50 images for testing.
3 The application could not adapt different stocking situation, for example, on-shelf boxes that contain nails to be sold could not be detected from front view.
The model is trained for the surveillance tool specifically from front angle of the shelf.
Implementation of model on CCTV that can cover more angle should be trained. Also,
implementation of model on fish-eye camera can be trained for placement on specific angle in the shelf to detect specific stock, e.g., small items in the box for hardware stores.
4 The application crashed multiple times under low connectivity.
The handshaking between Telegram and the backend system requires stable connection.
Include local alert system that does not require internet connection or implement better API resources.
This project is one of the basic foundation for building an unmanned shop and managing a large retail store. ED-App, Empty Shelf Detection Application is relatively simple and easy to be used, but should do more for real- world implementation. For example, it can be improved as a stock management application with product recognition system. With deep learning model that covers even variety training, it should be able to keep track and predict the product to be placed in the empty slot of the shelf.
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APPENDICES
Appendix A: Gantt Chart
Appendix B: User Satisfaction Results
Participant # 1
User Satisfactory Survey (adapted from System Usability Scale, Brooke, J. (1986))
No. Title
Strongly
Disagree Neutral Strongly Agree
1 2 3 4 5
1 I think that I would like to use this system to detect empty shelf.
/
2 I found the system unnecessarily complex.
/ 3 I thought the system was
easy to use
/ 4 I think that I would need
the support of a technical person to be able to use this system.
/
5 I found this system was easily moved through without a lot of
backtracking or data re- entry.
/
6 I thought there was too much inconsistency in this system.
/
7 I would imagine that most people would learn to use this website very quickly.
/
8 I found the system very awkward to use.
/ 9 I felt very confident using
the system.
/ 10 I needed to learn a lot of
things before I could get going with this system.
/
What did you like the best about the system? The system is capable to detect empty stock for necessary replenishment.
What did you like the least about the system? The system takes a little longer to load in the process of detecting the empty area of the stocks.
Do you have any other final comments or questions? None so far.
Participant # 2
User Satisfactory Survey (adapted from System Usability Scale, Brooke, J. (1986))
No. Title
Strongly
Disagree Neutral Strongly Agree
1 2 3 4 5
1 I think that I would like to use this system to detect empty shelf.
/
2 I found the system unnecessarily complex.
/ 3 I thought the system was
easy to use
/ 4 I think that I would need
the support of a technical person to be able to use this system.
/
5 I found this system was easily moved through without a lot of
backtracking or data re- entry.
/
6 I thought there was too much inconsistency in this system.
/
7 I would imagine that most people would learn to use this website very quickly.
/
8 I found the system very awkward to use.
/ 9 I felt very confident using
the system.
/ 10 I needed to learn a lot of
things before I could get going with this system.
/
What did you like the best about the system? Easy to use.
What did you like the least about the system? None.
Do you have any other final comments or questions? None.
Participant # 3
User Satisfactory Survey (adapted from System Usability Scale, Brooke, J. (1986))
No. Title
Strongly
Disagree Neutral Strongly Agree
1 2 3 4 5
1 I think that I would like to use this system to detect empty shelf.
/
2 I found the system unnecessarily complex.
/ 3 I thought the system was
easy to use
/ 4 I think that I would need
the support of a technical person to be able to use this system.
/
5 I found this system was easily moved through without a lot of
backtracking or data re- entry.
/
6 I thought there was too much inconsistency in this system.
/
7 I would imagine that most people would learn to use this website very quickly.
/
8 I found the system very awkward to use.
/ 9 I felt very confident using
the system.
/ 10 I needed to learn a lot of
things before I could get going with this system.
/
What did you like the best about the system? It can help me in managing my shelf and my stock.
What did you like the least about the system? The preview video is not smooth enough.
Do you have any other final comments or questions? Can improve the preview video smoothness to make it even user-friendly.