To address the high cost of detection equipment and slow detection speed of traditional algorithms for detecting point defects in laser soldering on the production line,we propose an improved YOLOv5s algorithm that can directly detect defects on the laser soldering equipment.By introducing GhostNetV2 convolution mechanism,the backbone network is lightweight improved,the parameter quantity of the original network model reduced and the detection speed increased.Simultaneously,omni-dimensional dynamic convolution module is used to improve both the feature extraction capability and detection accuracy of the model.The experimental results show that the improved YOLOv5s model has a reduced network parameter quantity of 23.89%compared to the original model.The mean average precision of improved model reached 95.0%on the self-made laser soldering point defect dataset and validation set,reflecting a 1 percentage point improvement over the original model.The detection rate increased by 12.62 frame/s on the experimental platform compared to the original model.Finally,the proposed algorithm is deployed on the laser soldering equipment and can detect corresponding soldering defects at a running speed of 42.2 frame/s,basically meet the real-time welding defect detection needs of laser soldering.
YOLOv5slaser soldering point defect detectiondeep learninglightweightingomni-dimensional dynamic convolution
NETL
NSTL
万方数据
