| Issue |
MATEC Web Conf.
Volume 355, 2022
2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)
|
|
|---|---|---|
| Article Number | 02054 | |
| Number of page(s) | 6 | |
| Section | Mathematical Science and Application | |
| DOI | https://doi.org/10.1051/matecconf/202235502054 | |
| Published online | 12 January 2022 | |
A package auto-counting model based on tailored YOLO and DeepSort techniques
1 School of E-business and Logistics, Beijing Technology and Business University, Beijing, 100048, China
2 National Engineering Laboratory for Agri-Product Quality Traceability, Beijing, 100048, China
3 School of Computer Science, Beijing Technology and Business University, Beijing, 100048, China
* Corresponding author: yipengzhou@163.com
In the industrial area, the deployment of deep learning models in object detection and tracking are normally too large, also, it requires appropriate trade-offs between speed and accuracy. In this paper, we present a compressed object identification model called Tailored-YOLO (T-YOLO), and builds a lighter deep neural network construction based on the T-YOLO and DeepSort. The model greatly reduces the number of parameters by tailoring the two layers of Conv and BottleneckCSP. We verify the construction by realizing the package counting during the input-output warehouse process. The theoretical analysis and experimental results show that the mean average precision (mAP) is 99.50%, the recognition accuracy of the model is 95.88%, the counting accuracy is 99.80%, and the recall is 99.15%. Compared with the YOLOv5 combined DeepSort model, the proposed optimization method ensures the accuracy of packages recognition and counting and reduces the model parameters by 11MB.
Key words: Object tracking / Object detection YOLOv5 / DeepSort / Compressed / Deep learning model
© The Authors, published by EDP Sciences, 2022
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