Chip Pin Defect Detection Based on Lightweight YOLOv8 Network
In the chip-on-chip assembly process,the quality of chip pins plays a decisive role in the success rate of assembly.Therefore,accurate detection of chip pin defects before assembly is crucial.To improve the efficiency and accuracy of detection,this paper proposes a detection method based on a lightweight YOLOv8 neural network.Firstly,the method uses depth maps generated by point cloud data projection as input data,which enables the network to extract the height information of chip pins from point cloud data,and obtain spatial dimension features of pins.To improve the detection speed,some convolution modules and C2f modules in the network structure are optimized into GSConv convolution and VoVGSCSP mod-ules,while the parameter-intensive feature fusion part and detection head in the original network are opti-mized into lightweight feature fusion networks and single-scale detection heads with fewer parameters.Fi-nally,attention mechanism(CBAM)is added to the corresponding parts of the network to improve the de-tection accuracy.Experimental results show that compared with the original network,the lightweight net-work reduces the network volume by 51.5% while maintaining the detection accuracy,and the single image detection speed is improved by 36.4% .
万方数据
维普
