Investigating the Impact of Image Quality Acquisition to Deep Object Detection Performance: a Case Study With PCB Damage Detection via Mobile Devices in the Wild

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Abstract
Mobile devices have revolutionized image acquisition, enabling diverse communication and image-processing applications. One promising application is automated damage detection in printed circuit boards (PCBs), crucial for quality control in electronics manufacturing. However, unlike controlled environments, mobile device image acquisition introduces challenges such as non-uniform lighting, background interference, and varying camera resolution, which can affect the accuracy of deep learning models. This paper presents a case study investigating the impact of domain-specific, no-reference image quality metrics on the performance of deep-learning object detection models for PCB damage detection. We evaluate nine metrics, including five novel contributions, using a semantic segmentation approach to measure foreground and background quality. Our study assesses how these metrics influence the performance of deep neural network architectures, determining optimal thresholds that separate high and low-quality images. Experiments are conducted on real-life images captured with smartphones in industrial settings. Our findings indicate that filtering poor-quality images based on these metrics could significantly improve detection performance, offering practical benefits for mobile damage detection applications.

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