# High-Precision Chip Detection Using YOLO-Based Methods
## 5 Conclusions  
During machining, workers pay attention to the quantity and shape of the chips generated in the process to assess the machining quality. Therefore, a machine vision-based method for the counting and monitoring of chips produced during turning operations may help to promote process optimization. However, detecting chips using the current object detection algorithms is challenging due to their generally small sizes during machining. This paper aimed to develop a lightweight model that accurately identified chips and their locations and counted the number of chip particles appearing in videos. First, upon introducing the GM module, the FPS value of YOLOv11 was improved, the number of parameters was reduced, and the FLOPs value was decreased, with only a limited drop in accuracy. Then, by using the workpiece as a reference during machining and identifying it, different NMS thresholds were applied to process the bounding boxes of chips based on their positions relative to the workpiece, leading to the development of DNMS. The results demonstrate that DNMS significantly enhances the accuracy of multiple versions of YOLO in chip detection. Finally, we also designed the video-level post-processing algorithm (VPPA), which implements a rising edge signal trigger mechanism to refine the counting rules for crack chips. Its purpose is to count the number of broken chip particles appearing in the video. This approach effectively monitors the quantity of crack chips produced during the machining processes of machine tools. It allows for the quantitative analysis of the monitored data, facilitating the assessment of the surface quality of processed workpieces. The model size and frames per second (FPS) serve as critical metrics in evaluating hardware resource consumption in models. According to the Ultralytics community documentation, YOLOv11n demonstrates compatibility with diverse embedded devices, including the RK3576 and RV1106 platforms. The model proposed in this study enhances the YOLOv11n architecture, achieving a more compact structure while maintaining comparable FPS. This optimized model significantly reduces the computational complexity without compromising the detection accuracy, exhibiting strong potential for the real-time monitoring of turning processes in embedded systems. However, this research presents the following limitations: (1) the absence of dual label assignment and consistent matching metric strategies necessitates a reliance on non-maximum suppression (NMS) during inference, limiting the detection speed (FPS); (2) machining tests were conducted without cutting fluid, leaving the algorithm's performance under cutting fluid interference unverified. Conceptualization, J.Z. ; methodology, R.L. ; software, R.L. ; validation, R.L. and J.Z. ; formal analysis, R.L. ; investigation, R.L. ; resources, R.L. ; data curation, R.L. ; writing--original draft preparation, J.Z. ; writing--review and editing, J.Z. ; visualization, R.L. ; supervision, J.Z. ; project administration, J.Z. ; funding acquisition, R.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Center for Balance Architecture, Zhejiang University, grant number K-20203312C and Construction Scientific Research Project in Zhejiang Province, grant number 2018K068. The raw data supporting the conclusions of this article will be made available by the authors on request. Author [PERSON] was employed by the company The Architectural Design & Research Institute of Zhejiang University Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.