# High-Precision Chip Detection Using YOLO-Based Methods
## 1 Introduction  
With the rise of dark factories, automated machining quality monitoring has become an essential requirement in modern manufacturing. To monitor the machining process, online measurements for various physical parameters have been widely used in factories. These parameters include the torque, acceleration, temperature, acoustic emission, displacement, and cutting force, which are critical in ensuring process optimization and product quality [1, 2]. Traditionally, experienced workers evaluated the processing quality and machine tool status by examining the shape and number of tuning chips. Recently, many studies have also explored the relationship between chips and processing quality. For example, [PERSON] et al. [3] tested the relationship between the chip morphology and surface quality of products in a high-speed hard-cutting experiment with PCBN tools on hardened steel GCr15. They found that tools with variable chamfered edges produced more regular and stable chips. However, tools with uniform chamfered edges experienced a transition inchip morphology from wavy to irregular curves over time. [PERSON] et al. (2018) found that, in the process of the heavy milling of 508 III steel, the debris morphology changed significantly at different stages of tool wear. Firstly, the chips are C-shaped at the beginning of wear; then, the shape changes to strip-like formations as the wear intensifies, ultimately becoming a spiral when the tool experiences severe wear. Research by [PERSON] (2018) found that a band-type chip could scratch the workpiece surface or damage cutting edges, while C-shaped chips could affect the surface roughness, and shattered chips tend to wear down the sliding surfaces of the machine tool. However, in practice, although the pixel quality and sampling rates of current cameras meet the requirements for chip monitoring, the automated monitoring of chips in factories to manage the processing conditions is not commonly observed. This is because two key challenges must be addressed: firstly, how to perform the target detection of chips; secondly, how to count the number of chip particles appearing in the video. The chips produced by machining exhibit diverse shapes and dimensions, including, but not limited to, spiral-shaped fragments and triangular-shaped fragments; this morphological complexity thus renders it challenging for conventional methods like template matching to accurately identify chips in photographic images. With the development of deep learning algorithms, many excellent object detection algorithms have emerged, which may be suitable for the detection of chips and processing of videos ([PERSON] et al., 2017). Paper (2018) established a correlation between post-detachment chips and the tool wear status using a simple convolutional neural network. In paper (2018), various deep learning algorithms, including a CNN, AlexNet, EfficientNetB0, MobileNetV2, CoAtNet-0, and ResNet18, are explored for the monitoring and measurement of wear through images of machining chips. Paper (2018) used the cutting temperature and chip characteristics with the neural network BP and LSTM methods to predict the tool life. However, research on accurate video-based chip quantification remains notably scarce in the existing literature. YOLO (You Only Look Once) is one of the most prominent and widely applied models in various fields, such as firefighting ([PERSON] et al., 2015), monitoring ([PERSON] et al., 2015), agricultural production ([PERSON] et al., 2015), defect detection ([PERSON] et al., 2015), and online wear tool monitoring ([PERSON] et al., 2015). The early YOLOv1 significantly improved the detection speed through its end-to-end single-stage detection framework, but its localization accuracy and adaptability to complex scenarios were relatively weak. Subsequent versions introduced optimizations from various perspectives. For instance, YOLOv3 ([PERSON] et al., 2015) enhanced the detection capabilities for small objects by incorporating multi-scale prediction and residual structures.