Glass substrates such as coverslips and electronic product glass are extensively used in laboratory, biomedical, and consumer electronics applications, where surface integrity is essential for ensuring functional reliability and experimental accuracy. Performance can be greatly impacted by even little Surface Defects (SD), such as stains, cracks, and scratches. In high-throughput settings, traditional manual inspection is subjective, time-consuming, and inefficient at identifying minute flaws. In order to overcome these obstacles, this study suggests a brand-new hybrid Deep Learning (DL) framework for automated glass SD detection and categorization called Attention-Boosted Convolution with Discrete Wavelet Transform (ATTBC-DWT).The method integrates Convolutional Neural Networks (CNN) to extract hierarchical spatial features, Discrete Wavelet Transform (DWT) to enhance multi-resolution defect representation and suppress noise, and an attention mechanism to emphasize defect-relevant regions while reducing background interference. Image normalization and Gaussian filtering are employed to improve contrast consistency and noise robustness. The coordinated merging of spatial, frequency-domain, and attention-based learning, which allows for exact localization and accurate recognition of defects of various sizes and forms, is what makes the proposed approach novel. In comparison to traditional CNN and detection models, experimental assessment utilizing Python-based DL tools shows better performance, with a 99.23% recall, enhanced precision, F1-score, and fewer false positives.The results confirm the robustness and effectiveness of the ATTBC-DWT framework for real-time inspection, supporting seamless integration into automated laboratory and industrial quality control systems.
Yang et al. (Thu,) studied this question.