融合LoG特征的凸焊螺母检测算法OA北大核心CSTPCD

In order to solve the problems of missing welding and miswelding of convex welding nuts in the common convex welding process for the connection of automobile curved parts,and the low efficiency detection method mainly relying on manual visual inspection,a visual target detection algorithm for welding nuts based on Faster-RCNN is pro-posed.Firstly,taking Faster-RCNN as the basic model,aiming at the problem that it is difficult to extract nut features from different angles,a method of adaptive fusion of artificial features and original image is proposed to enhance the ability of the model to extract nut features,and feature pyramid network(FPN)is introduced to solve the problem that small targets are difficult to be accurately detected.Then,in order to improve the detection robustness of the network in the com-plex background,the coordinate attention mechanism is embedded in the FPN to improve the attention of the network to the key targets.Finally,the loss function is designed to improve the training effect and enhance the regression accuracy of the central point of the regression box.The experimental results show that compared with the original algorithm,the average precision(AP)of convex welding nuts at IoU=0.75 increases 8.65 percentage points to 90.11%and the recall increases 5.87 percentage points to 79.23%,which is a significant improvement compared with the original algorithm.