High-Precision Chip Detection Using YOLO-Based Methods
Machining chips are directly related to both the machining quality and tool condition. However, detecting chips from images in industrial settings poses challenges in terms of model accuracy and computational speed. We firstly present a novel framework called GM-YOLOv11-DNMS to track the chips, foll...
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MDPI AG
2025-07-01
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| author | Ruofei Liu Junjiang Zhu |
| author_facet | Ruofei Liu Junjiang Zhu |
| author_sort | Ruofei Liu |
| collection | DOAJ |
| description | Machining chips are directly related to both the machining quality and tool condition. However, detecting chips from images in industrial settings poses challenges in terms of model accuracy and computational speed. We firstly present a novel framework called GM-YOLOv11-DNMS to track the chips, followed by a video-level post-processing algorithm for chip counting in videos. GM-YOLOv11-DNMS has two main improvements: (1) it replaces the CNN layers with a ghost module in YOLOv11n, significantly reducing the computational cost while maintaining the detection performance, and (2) it uses a new dynamic non-maximum suppression (DNMS) method, which dynamically adjusts the thresholds to improve the detection accuracy. The post-processing method uses a trigger signal from rising edges to improve chip counting in video streams. Experimental results show that the ghost module reduces the FLOPs from 6.48 G to 5.72 G compared to YOLOv11n, with a negligible accuracy loss, while the DNMS algorithm improves the debris detection precision across different YOLO versions. The proposed framework achieves precision, recall, and mAP@0.5 values of 97.04%, 96.38%, and 95.56%, respectively, in image-based detection tasks. In video-based experiments, the proposed video-level post-processing algorithm combined with GM-YOLOv11-DNMS achieves crack–debris counting accuracy of 90.14%. This lightweight and efficient approach is particularly effective in detecting small-scale objects within images and accurately analyzing dynamic debris in video sequences, providing a robust solution for automated debris monitoring in machine tool processing applications. |
| format | Article |
| id | doaj-art-1f0dd20788074be48eeb394d29cced18 |
| institution | DOAJ |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| series | Algorithms |
| spelling | doaj-art-1f0dd20788074be48eeb394d29cced182025-08-20T02:48:19ZengMDPI AGAlgorithms1999-48932025-07-0118744810.3390/a18070448High-Precision Chip Detection Using YOLO-Based MethodsRuofei Liu0Junjiang Zhu1Center for Balance Architecture, Zhejiang University, Hangzhou 310028, ChinaCollege of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, ChinaMachining chips are directly related to both the machining quality and tool condition. However, detecting chips from images in industrial settings poses challenges in terms of model accuracy and computational speed. We firstly present a novel framework called GM-YOLOv11-DNMS to track the chips, followed by a video-level post-processing algorithm for chip counting in videos. GM-YOLOv11-DNMS has two main improvements: (1) it replaces the CNN layers with a ghost module in YOLOv11n, significantly reducing the computational cost while maintaining the detection performance, and (2) it uses a new dynamic non-maximum suppression (DNMS) method, which dynamically adjusts the thresholds to improve the detection accuracy. The post-processing method uses a trigger signal from rising edges to improve chip counting in video streams. Experimental results show that the ghost module reduces the FLOPs from 6.48 G to 5.72 G compared to YOLOv11n, with a negligible accuracy loss, while the DNMS algorithm improves the debris detection precision across different YOLO versions. The proposed framework achieves precision, recall, and mAP@0.5 values of 97.04%, 96.38%, and 95.56%, respectively, in image-based detection tasks. In video-based experiments, the proposed video-level post-processing algorithm combined with GM-YOLOv11-DNMS achieves crack–debris counting accuracy of 90.14%. This lightweight and efficient approach is particularly effective in detecting small-scale objects within images and accurately analyzing dynamic debris in video sequences, providing a robust solution for automated debris monitoring in machine tool processing applications.https://www.mdpi.com/1999-4893/18/7/448ghost moduleYOLOv11DNMSchip detectionvideo |
| spellingShingle | Ruofei Liu Junjiang Zhu High-Precision Chip Detection Using YOLO-Based Methods Algorithms ghost module YOLOv11 DNMS chip detection video |
| title | High-Precision Chip Detection Using YOLO-Based Methods |
| title_full | High-Precision Chip Detection Using YOLO-Based Methods |
| title_fullStr | High-Precision Chip Detection Using YOLO-Based Methods |
| title_full_unstemmed | High-Precision Chip Detection Using YOLO-Based Methods |
| title_short | High-Precision Chip Detection Using YOLO-Based Methods |
| title_sort | high precision chip detection using yolo based methods |
| topic | ghost module YOLOv11 DNMS chip detection video |
| url | https://www.mdpi.com/1999-4893/18/7/448 |
| work_keys_str_mv | AT ruofeiliu highprecisionchipdetectionusingyolobasedmethods AT junjiangzhu highprecisionchipdetectionusingyolobasedmethods |