PCBDet: An efficient deep neural network object detection architecture for automatic PCB component detection on the edge

Abstract

There can be numerous electronic components on a given PCB, making the task of visual inspection to detect defects very time‐consuming and prone to error, especially at scale. There has thus been significant interest in automatic PCB component detection, particularly leveraging deep learning. While deep neural networks are able to perform such detection with greater accuracy, these networks typically require high computational resources, limiting their feasibility in real‐world use cases, which often involve high‐volume and high‐throughput detection with constrained edge computing resource availability. To bridge this gap between performance and resource requirements, PCBDet, an attention condenser network design that provides state‐of‐the‐art inference throughput while achieving superior PCB component detection performance compared to other state‐of‐the‐art efficient architecture designs, is introduced. Experimental results show that PCBDet can achieve up to 2× inference speed‐up on an ARM Cortex A72 processor when compared to an EfficientNet‐based design while achieving ∼2–4% higher mAP on the FICS‐PCB benchmark dataset.