Abstract In the realm of industrial automation, innovative AI-driven solutions are revolutionizing object detection and counting processes. This study presents a modified YOLOv10 model enhanced with the Ghost mechanism, including GhostConv and C3Ghost modules, designed to optimize computational efficiency while achieving superior detection accuracy. The proposed model excels in real-time applications, delivering a precision of 0.972, recall of 0.967, and mean Average Precision (mAP) scores of mAP@50=0.991 and mAP@50-95=0.799, all while reducing the parameter count to 6.5 M. These advancements address the challenges of fastener detection, particularly in cluttered environments and under diverse lighting conditions, paving the way for streamlined operations in manufacturing assembly lines. By leveraging specialized datasets tailored to factory-specific conditions and incorporating advanced algorithmic improvements, the model demonstrates its capacity to enhance inventory management and quality control processes. This study underscores the importance of lightweight yet robust AI models in modern manufacturing, setting a benchmark for scalable and efficient automation systems that cater to diverse industrial needs.
Ly et al. (Thu,) studied this question.
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