This invention discloses a method and
system for detecting solder joints on PCB circuit boards based on image recognition, belonging to the field of
image processing technology. The method first acquires and preprocesses 3D point clouds of PCB solder joints, and calculates the local geometric probability density. Non-uniform downsampling is performed using density inverse weighting, prioritizing the retention of sparse key features such as solder joint pins and removing redundant substrate data, outputting a set of physical feature points. This set is then input into a
feature extraction network, and strong correlations in feature dimensions are eliminated through local and global
forging mapping technology to construct isotropic
hypersphere spatial feature vectors. Finally, a multi-coverage discrimination model containing defective hyperellipsoids and normal hyperspheres is constructed based on local density and spatial features. The solder joint quality is comprehensively judged using an integrated voting strategy. This invention effectively solves the problems of
false detection and missed detection caused by sparse feature loss and sample imbalance, and significantly improves the detection accuracy of minute defects.