Abstract
Normally functioning and complete printed circuit board (PCB) can ensure the safety and reliability of electronic equipment. PCB defect detection is extremely important in the field of industrial inspection. For traditional methods of PCB inspection, such as contact detection, are likely to damage the PCB surface and have high rate of erroneous detection. In recent years, methods of detection through image processing and machine learning have gradually been put into use. However, PCB inspection is still an extremely challenging task due to the small defects and the complex background. To solve this problem, a lightweight one-stage defect detection network based on dual attention mechanism and Path Aggregation Feature Pyramid Network (PAFPN) has been proposed. At present, some methods of defect detection in industrial applications are often based on object detection algorithms in the field of deep learning. Through comparative experiments, compared with the Faster R-CNN and YOLO v3 which are usually used in the current industrial detection, the inference time of our method are reduced by 17.46 milliseconds (ms) and 4.75 ms, and the amount of model parameters is greatly reduced. It is only 4.42 M, which is more suitable for industrial fields and embedded development systems. Compared with the common one-stage object detection algorithm Fully Convolutional One-Stage Object Detection (FCOS), mean Average Precision (mAP) is increased by 9.1%, and the amount of model parameters has been reduced by 86.12%.
Highlights
As a carrier for connecting various electronic components, printed circuit board (PCB) is responsible for providing circuit connections and hardware support for the equipment
[3] Bing Hu [4] proposed a Faster R-CNN [5] detection algorithm based on ShuffleNetV2 [6] residual module and Guided Anchoring–Region Proposal Network (GA-RPN) optimization to detect several common types of PCB defects
Ran Guangzai [7]et al detected PCB defects based on the SSD [8] model, but the experiment only detected three types of defects and did not compare with other object detection methods based on deep neural networks
Summary
As a carrier for connecting various electronic components, PCB is responsible for providing circuit connections and hardware support for the equipment. In 2021, Lan Zhuo [9] proposed a detection algorithm based on the YOLOv3 model, Li Yuting [10] proposed a detection algorithm based on the fusion of HybridYOLOv2 and Faster R-CNN Both methods have high detection accuracy, but they have not considered the memory consumption in actual applications. In this regard, a lightweight one-stage defect detection network based on fusion attention mechanism and PAFPN [11] has been proposed. A lightweight Backbone neural network MobileNetV2 [12] has been applied to replace the commonly used Backbone: ResNet101 [13] in the FCOS, which greatly reduces model parameters and improves the real-time performance of the algorithm. The optimized intersection over union (IoU) function can consider the overlap rate, distance and ratio between the predicted box and the ground truth box, can directly minimize the distance, so that the convergence process is faster and the prediction bounding box regression becomes more stable
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