CHENG S S,WANG X,LI W,et al. Research on lightweight target detection algorithm based on improved YOLOv4[J]. Microelectronics & Computer,2023,40(6):1-8. doi: 10.19304/J.ISSN1000-7180.2022.0447
Citation: CHENG S S,WANG X,LI W,et al. Research on lightweight target detection algorithm based on improved YOLOv4[J]. Microelectronics & Computer,2023,40(6):1-8. doi: 10.19304/J.ISSN1000-7180.2022.0447

Research on lightweight target detection algorithm based on improved YOLOv4

  • Aiming at the problems of YOLOv4 target detection algorithm in some application scenarios with too many parameters, complex network and low accuracy, an improved lightweight target detection algorithm GD-YOLO was proposed. Firstly, the main feature extraction network of YOLOv4, CSPDarknet, is replaced by the lightweight network GhostNet, which greatly reduces the number of parameters and computation of the algorithm and makes the algorithm more lightweight. Secondly, the double attention mechanism (DATM) is proposed, which not only strengthens the spatial and channel features of the model, but also has a small number of structural parameters. The double attention mechanism is added to the three effective feature layers extracted from the backbone network to make the model more effective for feature extraction. Finally, ACON activation function was added to replace ReLU activation function in GhostNet network to further improve the detection accuracy of the algorithm. Experimental results on VOC2007+2012 data set show that the GD-YOLO algorithm has an average accuracy (mAP) of 84.28%, which is 4 percentage points higher than YOLOv4 algorithm and about 1 percentage point lower than YOLOv5 algorithm. Compared with YOLOv4 algorithm, the number of model parameters is reduced by 11M, and 3M compared with YOLOv5 algorithm. Compared with YOLOv4, the proposed GD-YOLO algorithm not only reduces the number of model parameters, but also preserves a higher average accuracy, indicating that the algorithm is more lightweight and has higher accuracy.
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