This study presents an artificial intelligence-based system for the real-time and high-accuracy detection of defects in bolts moving on a production line conveyor belt. Bolt defects are critical for product safety and production quality, while manual inspection methods remain insufficient due to their time-consuming nature, susceptibility to human error, and limited reliability. Although various approaches have been proposed in the literature, existing methods are limited in terms of small-object detection, real-time performance, and industrial integration. To address these limitations, a deep learning-based defect detection mechanism was developed and comparatively evaluated using the YOLOv9, YOLOv10, and YOLO11 architectures, with emphasis on low hardware requirements, ease of integration, and real-time capability. A custom dataset of 12,075 high-resolution conveyor belt bolt images was constructed and used to train and validate all model variants. Experimental results demonstrate that all three architectures achieved over 99% mAP50, with the YOLO11x model reaching 99.39% mAP50 and 90.90% mAP50-95, closely matched by YOLOv10x at 99.39% mAP50 and 90.91% mAP50-95. Considering both detection accuracy and inference speed, YOLO11x provides the most favorable balance for production line integration, while YOLOv10x stands out as a competitive alternative with comparable accuracy and lower computational cost. The proposed system offers a faster, more reliable, and easily integrable solution than manual inspection, with strong potential for adoption in high-precision industrial sectors such as automotive, aerospace, and defense.
Özkurt et al. (Mon,) studied this question.