<![CDATA[Purpose: Defect detection in industrial bolts is crucial for ensuring product reliability, production safety, and consistent quality control in modern industrial environments. However, visual inspection of metal bolts remains challenging due to low contrast, uneven lighting, and reflective surfaces that often hide subtle defect patterns and reduce detection accuracy. Most existing YOLO-based approaches focus on architectural modifications to improve performance, which may increase model complexity and limit real-time applicability. Methods: This study integrates Contrast-Limited Adaptive Histogram Equalization (CLAHE) with YOLOv8 to improve defect visibility prior to detection. CLAHE enhances local contrast by redistributing pixel intensities while suppressing noise amplification, thereby strengthening feature representation for deep learning-based detection. Experiments were conducted on a publicly available industrial bolt dataset annotated via Roboflow, using a 3-fold cross-validation strategy. Performance was assessed with Precision, Recall, mAP@50, mAP@50–95, FPS, and FLOPs to evaluate accuracy and real-time feasibility. Result: Experimental results based on a 3-fold cross-validation scheme indicate that the proposed CLAHE–YOLOv8 model achieves consistent performance improvements over the baseline YOLOv8 configuration. The method obtains an average Precision of 0.9495±0.0068, Recall of 0.9028±0.0235, mAP@50 of 0.9364±0.0156, and mAP@50–95 of 0.7121±0.0037, while maintaining real-time inference performance at 29.79 FPS. These results demonstrate that contrast-based preprocessing contributes positively to detection stability and localization consistency without increasing model complexity. Novelty: The novelty of this research lies in demonstrating that data-level contrast enhancement using CLAHE effectively improve industrial bolt defect detection performance without architectural modification, offering a practical and computationally efficient solution for real-time industrial inspection systems.]]>
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