Defect detection of printed circuit board based on adaptive key-points localization network

Many deep neural networks (DNNs) have been applied in the defect detection of products. Due to the irregular and small defects on printed circuit boards (PCB), it is difficult for the DNN-based defect detection models to achieve good detection performance. In this paper, a new DNN, adaptive key point localization network (AKPLNet) is proposed for PCB defect detection. Firstly, residual pyramid heat mapping network (RFHNet) that is composed of ResNet50_FPN and thermodynamic mechanism (TM), is used to perform multi-scale feature extraction and defect location. Secondly, an adaptive tree structure region proposal network (AT-RPN) based on tree structure Parzen estimation is proposed to obtain the predicted regions of the target, which reduces the need for large number of priori knowledge during the detection process. Finally, a key point regression algorithm is proposed to locate defects accurately. The defect detection performance of AKPLNet is validated on two PCB datasets. The mean average precision (mAP) of AKPLNet reaches 96.9% and 99.0% on PCB-Master dataset with the color images and DeepPCB-Master dataset with the grayscale images, improving 2.1% and 2.3% compared with Yolov7, respectively. The testing results demonstrate that AKPLNet achieves the better detection accuracy than those state-of-the-art methods.