Novel image-based model enhances the detection of surface defects in low-light industrial settings

Quality control (QC) is a critical component of industrial processes that ensures product reliability, quality, and safety.

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Anomaly detection (AD), which refers to the process of identifying outliers or rare/unusual events compared to the majority, is crucial for identifying defects during product inspection and QC. The increasing stringency in industrial regulations and rising demand for various products call for automated, robust, and efficient AD systems that can accurately detect anomalies. However, AD becomes particularly challenging using traditional methods, given the obscure and diverse environments in industrial settings, including low-light conditions. Moreover, AD models that rely on low-light image enhancement may be limited by artefacts and noisy images that do not accurately reflect subtle defects on industrial surfaces. Additionally, deep learning-based AD systems require extensive data processing and computational resources, which limit their widespread practical application.

To overcome this challenge, Dr Phan Xuan Tan, an Associate Professor at the Innovative Global Program, College of Engineering, Shibaura Institute of Technology, Japan, along with Dr Dinh-Cuong Hoang and other researchers from FPT University, Vietnam, have designed DarkAD—a novel end-to-end framework that can enhance AD in low-light industrial environments. The researchers have introduced a Dark-Aware Feature Adapter (DAFA) that integrates noise reduction and low-light image processing.