An efficient solder joint defects method for 3D point clouds with double-flow region attention network

In electronics mass-production, image-based methods are often used to detect the solder joint defects for achieving high-quality assurance with low labor costs. Recently, deep learning in 3D point clouds has shown an effective form of characterization for 3D objects. However, existing work rarely involves defect detection for PCBs based on 3D point clouds. In this paper, we propose a novel neural network named double-flow region attention network (DoubRAN) to detect defects of solder joints with 3D point clouds. On the one hand, a binocular lidar system is designed to efficiently capture 3D point clouds of solder joints. On the other hand, a fine-grained method named region attention network (RAN) is designed to detect defects, which attends on the region of interest directly by backpropagation without bounding box annotation. To evaluate the performance of our proposed network, we conduct extensive experiments on a unique dataset built by ourselves. The experimental results show that the region of interest extracted by RAN is consistent with the basis for human evaluation of solder joint quality. Besides, the defect detection results of DoubRAN meet factory requirements.

Automated summary

This paper proposes a novel neural network named DoubRAN to detect solder joint defects using 3D point cloud technology. The network utilizes a binocular lidar system to capture 3D point clouds and employs a fine-grained method called RAN for defect detection. Experimental results show that the network's performance aligns with the benchmark for human evaluation of solder joint quality and meets factory requirements.