# High-Precision Chip Detection Using YOLO-Based Methods
## 1 Introduction
YOLOv4 ([PERSON] et al., 2015) integrated self-attention mechanisms and cross-stage partial networks (CSPNet), which improved the feature representation while maintaining real-time performance. YOLOv7 ([PERSON] et al., 2015) proposed a scalable and efficient network architecture, utilizing reparameterization techniques to achieve parameter sharing. YOLOv8 ([PERSON] et al., 2015) further optimized the training process by introducing a dynamic label assignment strategy, which enhanced the convergence efficiency. YOLOv9 ([PERSON] et al., 2015) addressed the limitations of information loss and deep supervision in deep learning by introducing programmable gradient information (PGI) and a generalized efficient layer aggregation network (Gelan). YOLOv10 ([PERSON] et al., 2015) integrated global representation learning capabilities into the YOLO framework while maintaining low computational costs, significantly enhancing model performance and facilitating further improvements. In addition to version updates, numerous scholars have made improvements to various iterations of YOLO. For instance, [PERSON] et al. (2018) introduced the bidirectional feature pyramid network (BiFPN) into YOLOv5, enhancing its multi-scale feature fusion capabilities. [PERSON] et al. (2018) incorporated the convolutional block attention module (CBAM) into YOLOv7 and YOLOv8, while [PERSON] et al. (2018) integrated the squeeze-and-excitation (SE) module into YOLOv5, thereby improving the model's ability to focus on critical features. Furthermore, [PERSON] et al. proposed DIoU-NMS [24] based on YOLOv5, addressing the issue of missed detection in densely packed objects. These advancements have collectively contributed to the refinement and robustness of the YOLO framework. Moreover, the lightweight design of models has also garnered significant attention. For instance, [PERSON] et al. [25] proposed a sparsely connected asymptotic feature pyramid network, which optimized the architectures of YOLOv5 and YOLOv8. [PERSON] et al. [26] combined the PP-LCNet backbone network with YOLOv5, effectively reducing the model's parameter count and computational load. Among various lightweight strategies, the ghost module [27] has emerged as a notable technique, capable of generating the same number of feature maps as conventional convolutional layers but with significantly reduced computational costs. This module can be seamlessly integrated into YOLO networks to minimize the computational overhead. Previous studies have demonstrated the applicability of the ghost module in versions such as YOLOv5 and YOLOv8, achieving a reduction in model parameters while maintaining detection accuracy [28; 29]. However, to our knowledge, no research has yet explored the impact of integrating the ghost module into the more recent YOLOv11. Additionally, it is noteworthy that, starting from version 10, YOLO introduced the dual label assignment and consistent matching metric strategy [23]. Benefiting from this strategy, we can choose to use the original one-to-many head for training and then perform inference using the non-maximum suppression (NMS) algorithm or use a one-to-one detection head to directly obtain the inference results (also referred to as NMS-free training). Although NMS-free training results in lower inference latency, according to the literature [23], it requires more one-to-one matching discriminative features and may reduce the resolution of feature extraction. Machining chips exhibit significant variations in size and shape. Furthermore, the complex trajectories of flying chips during machining processes make accurately determining chip counts from video footage a significant computational challenge. In this paper, we introduce the ghost module into YOLOv11 and continue to employ the traditional one-to-many training approach. Additionally, we propose a novel dynamic non-maximum suppression (DNMS) algorithm to improve the accuracy of chip detection. Moreover, we present a post-processing method for chip counting in dynamic video sequences.