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Lightweight deep learning model for logistics parcel detection

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Abstract

Logistics parcel detection technology is critical for unmanned sorting. YOLOv5, the state-of-the-art (SOTA) object detection model, is a classic network widely used in engineering. However, on the premise of fast and accurate detection of logistics parcels, it confronts certain challenges of high computing load and parameter quantity. To address these issues, this paper proposes a two-scale lightweight deep learning model named SFYOLOv5. In this article, a lightweight feature extraction module called Pruned-Shuffle-Block (PSB) is proposed. Meanwhile, a double-layer pyramid structure for the medium and large target detection is created in accordance with the image size distribution of logistics parcels. With this structure, the Floating Point of Operations (FLOPs) and parameters in feature extraction were significantly reduced. In addition, a down sampling module named Focus for Downsampling (FFD) is designed and attention modules are introduced to extract high-level semantic information in logistics parcels. These modules not only compensate for the loss caused by down sampling but also improve the mean Average Precision (mAP). Finally, the comparison experiment is performed by using the self-built logistics parcel dataset. The results show that the mAP of the model reaches 99.1%, the number of model parameters decreased by 92.14%, and the FLOPs decreased by 89.57% compared with the existing SOTA model. This model can be used in logistics parcel intelligent sorting.

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Data availability

The datasets in this paper involve the privacy of the company, which is inconvenient to disclose, but other data may be obtained by reasonable request from the corresponding author.

References

  1. Chen, C.-L., Deng, Y.-Y., Weng, W., Zhou, M., Sun, H.: A blockchain-based intelligent anti-switch package in tracing logistics system. J. Supercomput. 77(7), 7791–7832 (2021). https://doi.org/10.1007/s11227-020-03558-7

    Article  Google Scholar 

  2. Wang, Q., Wu, B., Zhu, P., Li, P., Hu, Q.: Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

  3. Chen, W., Huang, H., Peng, S., Zhou, C., Zhang, C.: Yolo-face: a real-time face detector. Vis. Comput. 37(4), 805–813 (2021). https://doi.org/10.1007/s00371-020-01831-7

    Article  Google Scholar 

  4. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

  5. Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, vol. 28 (2015)

  6. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

  7. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)

  8. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A. C.: SSD: single shot multibox detector. In: European Conference on Computer Vision, pp. 21–37. Springer (2016). https://doi.org/10.1007/978-3-319-46448-0_2

  9. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y. M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. https://doi.org/10.48550/arXiv.2004.10934(2020)

  10. Jung, H.-K., Choi, G.-S.: Improved yolov5: efficient object detection using drone images under various conditions. Appl. Sci. 12(14), 7255 (2022). https://doi.org/10.3390/app12147255

    Article  Google Scholar 

  11. Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y. M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv preprint arXiv:2207.02696. https://doi.org/10.48550/arXiv.2207.02696(2022)

  12. Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)

  13. Ma, N., Zhang, X., Zheng, H.-T., Sun, J.: Shufflenet v2: practical guidelines for efficient CNN architecture design. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 116–131 (2018)

  14. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.-C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

  15. Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al.: Searching for mobilenetv3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1314–1324 (2019)

  16. Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430. https://doi.org/10.48550/arXiv.2107.08430 (2021)

  17. Clevert, D.-A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289. https://doi.org/10.48550/arXiv.1511.07289(2015)

  18. Elfwing, S., Uchibe, E., Doya, K.: Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw. 107, 3–11 (2018). https://doi.org/10.1016/j.neunet.2017.12.012

    Article  Google Scholar 

  19. Chen, Z., Wu, R., Lin, Y., Li, C., Chen, S., Yuan, Z., Chen, S., Zou, X.: Plant disease recognition model based on improved yolov5. Agronomy 12(2), 365 (2022). https://doi.org/10.3390/agronomy12020365

    Article  Google Scholar 

  20. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

  21. Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S.: Generalized intersection over union: a metric and a loss for bounding box regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 658–666 (2019)

  22. Zhang, Y.-F., Ren, W., Zhang, Z., Jia, Z., Wang, L., Tan, T.: Focal and efficient IOU loss for accurate bounding box regression. Neurocomputing 506, 146–157 (2022). https://doi.org/10.1016/j.neucom.2022.07.042

    Article  Google Scholar 

  23. Xu, X., Zhao, M., Shi, P., Ren, R., He, X., Wei, X., Yang, H.: Crack detection and comparison study based on faster R-CNN and mask R-CNN. Sensors 22(3), 1215 (2022). https://doi.org/10.3390/s22031215

    Article  Google Scholar 

  24. Saavedra, D., Banerjee, S., Mery, D.: Detection of threat objects in baggage inspection with x-ray images using deep learning. Neural Comput. Appl. 33(13), 7803–7819 (2021). https://doi.org/10.1007/s00521-020-05521-2

    Article  Google Scholar 

  25. Xue, J., Zheng, Y., Dong-Ye, C., Wang, P., Yasir, M.: Improved yolov5 network method for remote sensing image-based ground objects recognition. Soft Comput. (2022). https://doi.org/10.1007/s00500-022-07106-8

    Article  Google Scholar 

  26. Shu, X., Yang, J., Yan, R., Song, Y.: Expansion-squeeze-excitation fusion network for elderly activity recognition. IEEE Trans. Circuits Syst. Video Technol. (2022). https://doi.org/10.48550/arXiv.2112.10992

    Article  Google Scholar 

  27. Xi, P., Guan, H., Shu, C., Borgeat, L., Goubran, R.: An integrated approach for medical abnormality detection using deep patch convolutional neural networks. Vis. Comput. 36(9), 1869–1882 (2020). https://doi.org/10.1007/s00371-019-01775-7

    Article  Google Scholar 

  28. Tang, J., Shu, X., Yan, R., Zhang, L.: Coherence constrained graph LSTM for group activity recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2019). https://doi.org/10.1109/TPAMI.2019.2928540

    Article  Google Scholar 

  29. Wang, P., Wang, M., He, D.: Multi-scale feature pyramid and multi-branch neural network for person re-identification. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02653-5

    Article  Google Scholar 

  30. Shu, X., Zhang, L., Qi, G.-J., Liu, W., Tang, J.: Spatiotemporal co-attention recurrent neural networks for human-skeleton motion prediction. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 3300–3315 (2021). https://doi.org/10.1109/TPAMI.2021.30509182

    Article  Google Scholar 

  31. Yao, X., Zhang, J., Chen, R., Zhang, D., Zeng, Y.: Weakly supervised graph learning for action recognition in untrimmed video. Vis. Comput. (2022). https://doi.org/10.1007/s00371-022-02673-1

    Article  Google Scholar 

  32. Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

  33. Li, H., Xiong, P., An, J., Wang, L.: Pyramid attention network for semantic segmentation. arXiv preprint arXiv:1805.10180. https://doi.org/10.48550/arXiv.1805.10180 (2018)

  34. Hu, J., Zhi, X., Shi, T., Zhang, W., Cui, Y., Zhao, S.: Pag-yolo: a portable attention-guided yolo network for small ship detection. Remote Sens. 13(16), 3059 (2021). https://doi.org/10.3390/rs13163059

    Article  Google Scholar 

Download references

Funding

This study was funded by the Natural Science Foundation of Fujian Province (2020J05236), the Fujian Science and Technology Plan STS Project (2021T3069), Xiamen Key Laboratory Of Intelligent Manufacturing Equipment and Scientific Research Start-up Project of Xiamen University of Technology (YKJ20006R).

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Authors and Affiliations

  1. Fujian Key Laboratory of Green Intelligent Cleaning Technology and Equipment, School of Mechanical and Automotive Engineering, Xiamen University of Technology, Xiamen, 361024, China

    Guowei Zhang, Yangyang Kong, Wuzhi Li, Xincheng Tang, Weidong Zhang & Jing Chen

  2. Research and Development Department, Shunfeng Technology Co., Ltd, Xuefu Road, Shenzhen, 518000, Guangdong Province, China

    Li Wang

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  1. Guowei Zhang
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  2. Yangyang Kong
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  3. Wuzhi Li
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  4. Xincheng Tang
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  5. Weidong Zhang
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  6. Jing Chen
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  7. Li Wang
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Correspondence to Li Wang.

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Zhang, G., Kong, Y., Li, W. et al. Lightweight deep learning model for logistics parcel detection. Vis Comput 40, 2751–2759 (2024). https://doi.org/10.1007/s00371-023-02982-z

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