# High-Precision Chip Detection Using YOLO-Based Methods
## 4 Discussion  
During machining, experienced craftsmen can assess the quality of the process based on the number and shapes of chips produced. Therefore, it is essential to detect and quantify chips during the machining process. Papers [7; 8; 9] utilized deep learning algorithms to investigate the correlation between machining chips and tool wear, incorporating analyses of the chip morphology and color recognition. While some of these algorithms are deployable in online systems, research on the precise video-based quantification of chip volumes remains notably scarce in the existing literature. The use of an improved backbone and neck architecture enhances the feature extraction capabilities. However, because the chip is relatively small compared to the workpiece and the background, typically ranging from 0.4 mm to 0.7 mm or even smaller, and because industrial environments generally prefer industrial computers or embedded devices with high requirements in terms of software reliability and real-time performance, the use of YOLO for chip detection poses certain challenges. Table 2 describes the performance of various versions of the YOLO model, with parameter sizes of around 3 MB or less, in chip monitoring. Table 5 provides seven metrics, evaluating both the accuracy and real-time performance of the YOLO model and its improved versions. From Table 5, it can be observed that YOLOv11 achieved the highest values in precision, recall, and [EMAIL_ADDRESS], indicating its strong performance in terms of accuracy. YOLOv11 employs an improved backbone and neck architecture, enhancing the feature extraction capabilities, which may be the reason for its higher accuracy in detecting smaller objects. Additionally, from Table 2, we can observe that, with the introduction of the ghost module into YOLOv11, the model's accuracy slightly decreased. Compared to YOLOv11, GM-YOLOv11 shows lower values in terms of precision, recall, and [EMAIL_ADDRESS] by 0.31%, 1.46%, and 0.87%, respectively; GM-YOLOv11-backbone, compared to YOLOv11, exhibits lower values for precision, recall, and [EMAIL_ADDRESS] by 0.51%, 1.34%, and 1.05%, respectively. From Table 6, it can be seen that this decrease in accuracy does not affect the final statistical results of chip counting in the video. When using the error statistic, GM-YOLOv11 differs by only one count compared to YOLOv11, while GM-YOLOv11-backbone differs by only three counts compared to YOLOv11. However, the introduction of the ghost module significantly reduces the computational complexity, as shown in the Table 2. The FLOPs value for GM-YOLOv11 is 5.72G, while that of YOLOv11 reaches 6.48 G. Many scholars [28; 29; 32; 33; 34] have previously demonstrated in earlier versions such as YOLOv8 and YOLOv5 that introducing the ghost module can result in similar accuracy while significantly reducing the computational loads of YOLO models. This study applied it to YOLOv11 and found that similar conclusions hold even in the detection of smaller chips. In turning operations, the workpiece is generally much larger in size compared to the chips and has more distinct features, making it relatively easier to identify. The DNMS method proposed in this paper classifies the chips based on the position of the workpiece and then processes the extracted bounding boxes using different NMS thresholds. Comparing Tables 2 and 3, the accuracy of different versions of the YOLO algorithm was improved after adopting DNMS. For the precision metric, the highest improvement was observed in the GM-YOLOv11 model, which saw an increase of 3.24%, while the lowest improvement was in the YOLOv9t model, which increased by 1.16%. For recall, the most significant improvement was in GM-YOLOv11, where it increased by 2.42%, while the smallest improvement was in GM-YOLOv11-neck, where it increased by 0.7%. For [EMAIL_ADDRESS], the YOLOv5 model showed the largest increase at 1.89%, and YOLOv9t showed the smallest increase at 1.1%.