Comparative Study of YOLOv5, YOLOv8, and YOLOv11 for Dust Detection in Voice Coil Motor Assembly

Abstract:

Detecting microscopic defects in precision manufacturing remains a major challenge, particularly in hard disk drive (HDD) production where sub-millimeter dust particles on the Voice Coil Motor Assembly (VCMA) can cause performance degradation or early device failure. This study presents a comparative evaluation of three YOLO object-detection architectures—YOLOv5, YOLOv8, and YOLOv11—applied to high-resolution dust detection on VCMA components. All models were trained and tested using the same annotated 5-megapixel dataset under identical experimental settings to ensure fair comparison. The results show that YOLOv5 achieved the highest precision (0.640) and the highest mAP50–95 (0.253), indicating stable localization performance across strict IoU thresholds. YOLOv8 produced the highest mAP50 (0.500), reflecting strong localization accuracy at IoU 0.5, while maintaining moderate precision (0.633) and lower recall (0.455). YOLOv11 obtained the highest recall (0.636), successfully capturing the largest proportion of true dust particles, though with lower precision (0.335) and weaker mAP values, revealing a higher rate of false detections. Overall, the findings highlight clear trade-offs among the models: YOLOv5 offers the most balanced performance, YOLOv8 excels in spatial localization, and YOLOv11 is suitable for scenarios where maximum defect coverage is prioritized. These insights support the selection of appropriate detection architectures for automated micro-defect inspection and contribute to the development of AI-driven quality-control systems in HDD manufacturing.

• Periodical: Applied Mechanics and Materials (Volume 935)
• Pages: 207-212
• DOI: https://doi.org/10.4028/p-5lKwG3
• Online since: April 2026
• Authors: Kreetiwat Chaiyasin, Veena Phunpeng*, Sorada Khaengkarn, Kitsana Khodcharad, Tanporn Hengmeechai
• Keywords: Artificial Intelligence, Computer Vision, Object Detection, Voice Coil Motor Assembly (VCMA), You Only Look Once (YOLO)

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