Multi-threading method for rapid tool wear detection based on integrating image classification and object detection

Zhichao You; Yuheng Du; Xi Wang; Ziteng Li; Huan Liu; Duo Li

doi:10.1117/12.3032861

4 December 2024 Multi-threading method for rapid tool wear detection based on integrating image classification and object detection

Zhichao You, Yuheng Du, Xi Wang, Ziteng Li, Huan Liu, Duo Li

Author Affiliations +

Proceedings Volume 13283, Conference on Spectral Technology and Applications (CSTA 2024); 132830A (2024) https://doi.org/10.1117/12.3032861
Event: Conference on Spectral Technology and Applications (CSTA 2024), 2024, Dalian, China

Abstract

This research aims to develop a multi-threading method for rapid tool wear detection by integrating image classification and object detection techniques to address the challenge of tool wear detection. The research proposes a two-stage method that leverages a fast image classification model (VGG-16) and a high-accuracy object detection model (YOLOv5)to enable efficient multi-threading detection of tool wear regions across a large number of the flank wear images. The experiment results reveal that, when the wear images account for less than 70% of the total, this method can achieve detection speeds exceeding that of YOLOv5 while maintaining comparable detection accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhichao You, Yuheng Du, Xi Wang, Ziteng Li, Huan Liu, and Duo Li "Multi-threading method for rapid tool wear detection based on integrating image classification and object detection", Proc. SPIE 13283, Conference on Spectral Technology and Applications (CSTA 2024), 132830A (4 December 2024); https://doi.org/10.1117/12.3032861

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