A Visual Recognition Method for Stacked Plates Based on Deep Learning

This paper addresses the problem of counting stacked components in industrial scenarios and proposes a method that combines close-range scanning for complete contour acquisition with deep learning for quantity recognition: The contour acquisition system consists of a line array camera and a linear laser. Both are arranged horizontally at a certain angle, and the laser line is perpendicular and in the same direction as the stacking of the components. The system scans and connects single-row pixels along the stacking direction to obtain the contour. This method effectively avoids the occlusion problem caused by uneven stacking of components. The quantity recognition algorithm adopts a network structure similar to Encoding–Decoding using the component gap (cls: 0 indicates not, 1 indicates yes) and the endpoint coordinates of the separation line segment [cls, x1, y1, x2, y2] to form a label. Multi-scale anchors are introduced to predict the translation distance of the line segment (positive or negative, indicating direction). The prediction head is fully convolutional, and the loss for regression is computed using the predicted endpoints of the ground-truth line segments. A line segment redundancy removal method is proposed to output the predicted confidence (conf) and coordinates [conf, px1, py1, px2, py2] for each component gap. The self-built dataset is used for training and validation. Experiments show that the recognition accuracy of each image reaches 95.79%, and the gap recognition accuracy reaches 99.62%, which can meet the requirements of automation.