Abstract
Garbage sorting plays a very important role in ensuring a life safety. Aiming at the problems of poor real-time detection and low recognition accuracy of current garbage classification, an improved multi-objective real-time garbage classification recognition algorithm based on YOLOv5s is proposed. By combining the Coordinate Attention (CA) self-attention module and the neck part of YOLOv5s, the defect of insufficient receptive field is reduced to improve the detection accuracy, and the DeepSort algorithm is introduced to optimize the multi-target garbage feature recognition and strengthen the real-time detection ability. The experimental results show that the improved YOLOv5s garbage classification detection model can effectively identify 44 different types of garbage. Compared with the original YOLOv5s algorithm, the detection mAP value is 76.56%, an increase of 11.3%, and the precision is 85.63%, an increase of 12.38%. While ensuring the multi-target real-time detection efficiency, the improved algorithm can have better detection accuracy Rate.
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Acknowledgments
This work was supported in part by the National Key R&D Program of China under Grant nos. 2022YFE010300 and 2019YFE0122600, in part by the Major Project for New Generation of AI under Grant no. 2018AAA0100400, in part by the Natural Science Foundation of Hunan Province under Grant no. 2021JJ50050 and 2022JJ50051, in part by the Scientific Research Fund of Hunan Provincial Education Department under Grant nos. 21A0350 and 21C0439 and in part by the Hunan Provincial Innovation Foundation For Postgraduate under Grant nos.CX20220835.
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Huang, H., Xiao, F., Zhang, X., Yan, W., Liu, F., Wu, Y. (2023). Garbage Recognition Algorithm Based on Self-attention Mechanism and Deep Sorting. In: Wang, G., Choo, KK.R., Wu, J., Damiani, E. (eds) Ubiquitous Security. UbiSec 2022. Communications in Computer and Information Science, vol 1768. Springer, Singapore. https://doi.org/10.1007/978-981-99-0272-9_35
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