PCBDet: An Efficient Deep Neural Network Object Detection Architecture for Automatic PCB Component Detection on the Edge

TL;DR

PCBDet is a novel deep neural network architecture optimized for fast and accurate PCB component detection on edge devices, addressing the computational constraints of manufacturing environments.

Contribution

The paper introduces PCBDet, an attention condenser network that significantly improves inference speed and detection accuracy for PCB components on resource-limited edge hardware.

Findings

PCBDet achieves up to 2× inference speed-up on ARM Cortex A72.

PCBDet outperforms EfficientNet-based designs with 2-4% higher mAP.

Experimental results validate PCBDet's efficiency and accuracy in PCB inspection tasks.

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. However, deep neural networks typically require high computational resources, possibly limiting their feasibility in real-world use cases in manufacturing, which often involve high-volume and high-throughput detection with constrained edge computing resource availability. As a result of an exploration of efficient deep neural network architectures for this use case, we introduce 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. Experimental results show that PCBDet can achieve up to 2$\times$ inference speed-up on an ARM Cortex A72 processor when compared to an EfficientNet-based design while achieving $\sim$2-4\% higher mAP on the FICS-PCB benchmark dataset.