A deep context learning based PCB defect detection model with anomalous trend alarming system

The quality of a printed circuit board (PCB) is paramount towards ensuring proper functionality of electronic products. To achieve the required quality standards, substantial research and development efforts were invested to automate PCB inspection for defect detection, primarily using computer vision techniques. Despite these advancements, the accuracy of such techniques is often susceptible towards varying board and component size. Efforts to increase its accuracy especially for small or tiny defects on a PCB often lead to a tradeoff with reduced real-time performance, which in turn limits its applicability in the manufacturing industry. Hence, this paper puts forward an enhanced deep learning network which addresses the difficulty in inferring tiny or varying defects on a PCB in real-time. Our proposed enhancements consist of i) A novel multi-scale feature pyramid network to enhance tiny defect detection through context information inclusion; and ii) A refined complete intersection over union loss function to precisely encapsulate tiny defects. Experimental results on a publicly available PCB defects dataset demonstrate that our model achieves 99.17% mean-average precision, while maintaining real-time inferencing speed at 90 frames per second. In addition, we introduce three trend detection algorithms which alert an operator when abnormal development of defect characteristics is detected. Each algorithm is responsible for localizing defect buildups, increasing defect size and increasing defect occurrences, respectively. As a whole, the proposed model is capable of performing accurate and reliable real-time PCB inspection with the aid of an automated alert capability. The dataset and trained models are available at: https://github.com/JiaLim98/YOLO-PCB.