An Improved Dilated-Transposed Convolution Detector of Weld Proximity Defects

Abstract

Weld proximity defects present some characteristics, including mutual occupancy areas, high density, and small-target sizes, which is a challenge to detect industrial sheet welding accurately. The existing detectors are limited by the fixed size of feature receptive fields or the ability to process imbalanced positive-negative samples, which cannot be applied to detecting weld proximity defects. To solve the above-mentioned problem, we propose an industrial detector based on the Decoupled-Dilated-Transposed You Only Look Once v5 (DDT-YOLOv5). First, the Dilated-Transposed Convolution Cross-Stage Partial Network (DT-CSPnet) is designed in the DDT-YOLOv5, replacing the conventional cross-stage partial structure, to extract defect features effectively. Two middle Resblock Bodies in the backbone are designed as two DT-CSPnet blocks respectively, which can accelerate the convolutional feature extraction process. Second, we provide an improved Sigmoid Linear Unit function to reduce the loss of precision and optimize the gradient of the detection network. Third, the Squared-Focal Loss with the $\alpha $ -balanced method is explored in the decoupled head, including the Maxpooling layer. It aims for the accurate detection and classification of weld proximity defects in imbalanced samples. Finally, we contrast the proposed DDT-YOLOv5 with other related models via the real dataset of weld proximity defects. Experimental results show that the proposed model can present superior effects for detecting weld proximity defects.

Keywords

• Weld proximity defects
• dilation transposed convolution
• multi-object detection
• machine learning