Journal of Guangxi Normal University(Natural Science Edition) ›› 2023, Vol. 41 ›› Issue (3): 80-90.doi: 10.16088/j.issn.1001-6600.2022100804

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Lightweight Garbage Detection Method Based on Improved YOLOX

LI Yang1.2, GOU Gang1,2*   

  1. 1. State Key Laboratory of Public Big Data (Guizhou University), Guiyang Guizhou 550025, China;
    2. College of Computer Science and Technology, Guizhou University, Guiyang Guizhou 550025, China
  • Received:2022-10-08 Revised:2022-11-30 Online:2023-05-25 Published:2023-06-01

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Abstract: Household garbage classification is an effective measure to protect the ecological environment and promote green and harmonious development. Aiming at the problems such as limited computing resources and memory, and difficulty in embedding heavyweight models into mobile devices, a lightweight garbage classification detection method based on improved YOLOX-tiny is proposed in this paper. Firstly, the original IoU loss function is replaced by EIoU, which can accelerate the convergence and improve the detection accuracy. Secondly, the attention mechanism CBAM is introduced into the neck network to redistribute the weight of different channels to obtain more shallow fine-grained features and deep semantic information. Finally, the GhostBottleneck module is used to replace the CSP module in the feature picking network, which tends to retain more edge information, reduce the number of parameters, and lighten the model. Experimental results on Huawei cloud garbage dataset show that compared with YOLOX-tiny, the number of parameters of the improved algorithm is reduced to 87.97% of the original, the accuracy is increased by 0.3%, and the experimental effect on TrashNet dataset is increased by 0.36%, which proves the effectiveness of the proposed algorithm. The algorithm is conducive to the use of embedded mobile devices and has certain practical value.

Key words: garbage classification, YOLOX, lightweight network, EIoU, CBAM, GhostBottleneck

CLC Number:  TP391.41
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