Precise visual detection method for 3C assembly scenarios based on DETR
In order to address the issue of low recognition accuracy for small-scale components in 3C intelligent assembly scenarios,as well as problems such as missed detections when facing occluded target components and lighting variations,this paper proposes an improved DETR algorithm.To address the problem of low recognition accuracy for small-scale components,a multi-scale feature fusion network,PANet,is introduced to preserve more details and contextual information and enhance perception capability for small targets.To tackle the issue of weak multi-scale feature extraction capability and high computational complexity of the backbone network,ResNeSt-50 is employed to improve feature representation,enhance generalization,and achieve higher model efficiency.To overcome the problem of feature information vanishing in the negative half-axis caused by activation functions,the ACON adaptive activation function is used effectively to address this issue.Finally,Smooth-L1 Loss,in combination with the Focal Loss,is employed to achieve higher convergence accuracy and effectively address the problem of class imbalance.Experiments were conducted on a self-constructed 3C assembly dataset,and the experimental results demonstrate that the proposed algorithm improves the mAP@0.5 compared to the baseline network YOLOv5 by 4%and outperforms YOLOv7 by 2%.
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