Omslagsbillede

High-Precision Chip Detection Using YOLO-Based Methods

Guardado en:
Udgivet i:Algorithms vol. 18, no. 7 (2025), p. 448-471
Hovedforfatter: Liu Ruofei
Andre forfattere: Zhu Junjiang
Udgivet:
MDPI AG
Fag:
Accuracy
Video post-production
Deep learning
Agricultural production
Ghosts
Machining
Neural networks
Machine tools
Computing costs
Algorithms
Video data
Images
Modules
Debris
Automation
Object recognition
Morphology
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Resumen: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.
ISSN:1999-4893
DOI:10.3390/a18070448
Fuente:Engineering Database