Improved the lightweight part identification method of YOLOv5

Qi Qin; Li-Gang Zhao; Jun-Tao Yuan

doi:10.1117/12.2684959

19 October 2023 Improved the lightweight part identification method of YOLOv5

Qi Qin, Li-Gang Zhao, Jun-Tao Yuan

Proceedings Volume 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023); 127091D (2023) https://doi.org/10.1117/12.2684959
Event: Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 2023, Nanjing, China

Abstract

Aiming at the defects of the existing part recognition methods in the industrial production process when identifying small and dense parts, and the problem that the number of model parameters and large volume are difficult to deploy on the mobile platform, an improved YOLOv5 lightweight part target recognition model is proposed. On the basis of the YOLOv5 model, the Ghost module is first used to replace the backbone network to reduce the number of parameters of the model. Secondly, a SPP_F feature pyramid structure is constructed to speed up the training speed of the model and strengthen the ability of the model to extract information. Remove the detection layer for large target scales, thereby speeding up model training and detection; Finally, the GIOU function in the YOLOv5 model is replaced with the Alpha-SIOU function to improve the positioning accuracy of the bounding box. Experimental results show that the average accuracy (mAP) of the improved model reaches 99.5%, and the volume only accounts for 3.2 MB, which is 35% faster than the original YOLOv5 model, reducing hardware costs and meeting the needs of deployment in low-computing equipment.

Citation Download Citation

Qi Qin, Li-Gang Zhao, and Jun-Tao Yuan "Improved the lightweight part identification method of YOLOv5", Proc. SPIE 12709, Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023), 127091D (19 October 2023); https://doi.org/10.1117/12.2684959

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KEYWORDS

Detection and tracking algorithms

Convolution

Data modeling

Feature extraction

Performance modeling

Object detection

Target detection

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