ADPA-PCB: Enhancing PCB Defect Detection Neural Networks with Adaptive Activate Conv and PCBSAdd

The effective operation of electronic products heavily relies on the utilization of high-quality printed circuit boards PCBs . PCB defects can lead to failures, resulting in substantial wastage and financial losses. Hence, it is imperative to employ efficient technologies for detecting defects in PCBs, aiming to minimize waste and ensure product reliability. However, existing defect detection methods encounter challenges in achieving a balance between accuracy and speed. Traditional approaches often compromise one aspect in favor of the other, leading to suboptimal performance and limited practicality in real-world scenarios. To address this challenge, this paper presents a novel defect detection method. Our study proposes the augmentation of publicly available datasets of PCB images by incorporating a broader range of defect types. This augmentation facilitates better simulation of real-world defect detection scenarios. Additionally, we introduce the Adaptive Activate Conv module to enhance the model's capacity to learn features associated with PCB defects. Furthermore, we propose the PCBSAdd module to improve the model's accuracy in detecting PCB defects. Extensive experiments are conducted using an expanded PCB dataset to evaluate the performance of the proposed method. The results demonstrate outstanding performance, exhibiting a noteworthy 2.1 increase in detection accuracy. Moreover, the proposed model maintains real-time applicability, thereby highlighting its practical significance in industrial settings.