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SPSO-Pruner: a network pruning method on YOLOv5 for fewer categories scenarios

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Abstract

In recent years, object detection methods based on deep learning have developed rapidly, but we have found that comparing with public datasets, the categories of objects to be recognized in most practical scenarios are relatively small. In such scenarios, we proposed a network pruning method inspired by the PSO algorithm, named SPSO-Pruner to improve the detection accuracy and speed, while reducing the model parameters. To better fit the application scenarios, we used the up-to-date one stage detector - YOLOv5 to adapt to the real-time requirements. Comparing with regular network prune method such as slimming prune, our SPSO-pruner can reach better accuracy with less than 50% parameters. Besides, we proposed an optimization method for confidence Loss of YOLOv5 which can balance the Precision and Recall of our model, the F1 of our method is 4% higher than the baseline model.

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Funding

National Natural Science Foundation of China (CN) (61002011).

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Authors and Affiliations

  1. The State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yifei Zhang, Xinchen Liu, Yan Chen, Linyao Xie & Hongbo Wang

  2. Centre Borelli UMR 9010, Université Paris Cité, Paris, 75006, France

    Osman Salem

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  1. Yifei Zhang
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  2. Xinchen Liu
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  3. Yan Chen
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  4. Linyao Xie
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  5. Hongbo Wang
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  6. Osman Salem
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Correspondence to Hongbo Wang.

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Zhang, Y., Liu, X., Chen, Y. et al. SPSO-Pruner: a network pruning method on YOLOv5 for fewer categories scenarios. Multimed Tools Appl 83, 11493–11506 (2024). https://doi.org/10.1007/s11042-023-16038-w

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  • DOI: https://doi.org/10.1007/s11042-023-16038-w

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