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Спутники
РН
SpaceX
Роскосмос
NASA
СпутникиРНSpaceXРоскосмосNASAPublication date: 13 April 2026
Source: Applied Mechanics and Materials Vol. 935
Author(s): Kreetiwat Chaiyasin, Veena Phunpeng, Sorada Khaengkarn, Kitsana Khodcharad, Tanporn Hengmeechai
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.
[1] Q. Mao, S. Li, X. Hu, and X. Xusheng, "Coal Mine Belt Conveyor Foreign Objects Recognition Method of Improved YOLOv5 Algorithm with Defogging and Deblurring," Energies, vol. 15, p.9504, 12/14 2022.
DOI: 10.3390/en15249504
[2] R. Khanam, T. Asghar, and M. Hussain, "Comparative Performance Evaluation of YOLOv5, YOLOv8, and YOLOv11 for Solar Panel Defect Detection," Solar, vol. 5, no. 1.
DOI: 10.3390/solar5010006
[3] M. A. R. Alif, "Yolov11 for vehicle detection: Advancements, performance, and applications in intelligent transportation systems," arXiv preprint arXiv:2410.22898, 2024.
[4] L.-h. He, Y.-z. Zhou, L. Liu, W. Cao, and J.-h. Ma, "Research on object detection and recognition in remote sensing images based on YOLOv11," Scientific Reports, vol. 15, 04/23 2025.
[5] R. Khanam and M. Hussain, "Yolov11: An overview of the key architectural enhancements," arXiv preprint arXiv:2410.17725, 2024.
[6] T. Ye et al., "YOLO-FIX: Improved YOLOv11 with Attention and Multi-Scale Feature Fusion for Detecting Glue Line Defects on Mobile Phone Frames," Electronics, vol. 14, no. 5.
[7] L. He, Y. Zhou, L. Liu, and J. Ma, "Research and Application of YOLOv11-Based Object Segmentation in Intelligent Recognition at Construction Sites," Buildings, vol. 14, no. 12.
[8] N. S. Albalawi, "High-Precision Multi-Class Object Detection Using Fine-Tuned YOLOv11 Architecture: A Case Study on Airborne Vehicles," International Journal of Advanced Computer Science and Applications, vol. 16, no. 1, 2025.
[9] Y. Jia et al., "Hybrid-YOLOv5 for object detection of non-ferrous metals in end-of-life vehicles," Scientific Reports, vol. 15, 07/02 2025.
[10] A. S. Geetha and M. Hussain, "A comparative analysis of yolov5, yolov8, and yolov10 in kitchen safety," arXiv preprint arXiv:2407.20872, 2024.
[11] S. Prez, C. Gmez, and M. Rodrguez, "Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms," arXiv preprint arXiv:2410.10096, 2024.
[12] M. Abozahhad and M. Abo-Zahhad, "Real time intelligent garbage monitoring and efficient collection using Yolov8 and Yolov5 deep learning models for environmental sustainability," Scientific Reports, vol. 15, 05/08 2025.
[13] J. Terven, D.-M. Crdova-Esparza, and J.-A. Romero-Gonzlez, "A comprehensive review of yolo architectures in computer vision: From yolov1 to yolov8 and yolo-nas," Machine learning and knowledge extraction, vol. 5, no. 4, pp.1680-1716, 2023.
DOI: 10.3390/make5040083