Object detection of double-sided copper laminates based on YOLOv5

A YOLOv5-based object detection inventory algorithm was proposed to address the problem of low efficiency and high cost of inventorying the number of stacked compact double-sided laminates by manual means. The algorithm first collected images of laminates in a real factory environment and annotated them with the network; then constructed a double-sided copper clad laminate inspection dataset with a real application background and trained the dataset with the YOLOv5 object detection inventory algorithm to obtain a training model; finally, detected the number of laminates inventoried based on the validation set. The experimental data verified that the trained model could achieve 99% accuracy in detecting different numbers of sheets in stacked scenarios, which own more advantageous than manual inventory methods in terms of both detection accuracy and speed. The results show that the proposed YOLOv5-based object detection counting algorithm is reasonable, feasible and effective for counting stacked compact double-sided copper clad laminates.