|
In order to solve the problem of low detection accuracy caused by dense placement of PCB industrial equipment and occlusion overlap in industrial AR application system, an industrial equipment object detection algorithm based on improved YOLOv7 was proposed. Firstly, Coordinate attention is added into the network to strengthen the network's attention to the visible regional features of industrial equipment. Then SIoU loss function was used to improve the model loss function, which increased the convergence speed and effectively improved the regression accuracy of object position. Finally, Adaptive-NMS is introduced to adjust the threshold adaptively according to the density of objects so as to retain more correct prediction boxes. Based on the PCB industrial equipment dataset constructed by the author, the field test results verified that the proposed algorithm would be better than other 6 advanced object detection algorithms such as Fast R-CNN, and its object detection accuracy could reach 94.21%, showing significant computational advantages for the processing of occlusion equipment. The research result shows that the industrial equipment object detection algorithm based on the improved YOLOv7 is reasonable, feasible and effective, and the proposed method is more usable.
|
