# High-Precision Chip Detection Using YOLO-Based Methods
## 2 Materials and Methods  
### Model Architecture  
#### 2.1.1 YOLOv11  
YOLO (You Only Look Once) is a one-stage object detection algorithm. Its core idea is to model the object detection task as an end-to-end regression problem. It directly predicts the bounding boxes and class probabilities of all objects in an image through a single forward pass. Compared to algorithms like R-CNN, YOLO can leverage global contextual information and reduce computational resource usage. Thus, it is suitable for real-time monitoring tasks. YOLOv11 introduces numerous improvements over its predecessors compared to the previous version. The key advantages of YOLOv11 include better feature extraction, optimized efficiency and speed, fewer parameters with higher accuracy, cross-environment adaptability, and support for a wide range of tasks. The same version of YOLO incorporates suffixes like n, s, m, l, and x (denoting nano, small, medium, large, and extra large, respectively) to represent models of varying depth, width, size, and computational complexity. The architecture of YOLOv11n is shown in Figure 1.  
The detailed network structure and model parameter information of YOLOv11n used in this paper are shown in Figure 1. YOLOv11n consists of three critical components: the backbone, neck, and head. The backbone is responsible for extracting key features at different scales from the input image. This component consists of multiple convolutional blocks (Conv), each of which contains three sub-blocks, as illustrated in the 'a' block in Figure 1: Conv2D, BatchNorm2D, and the SiLU activation function to mitigate the effects of gradient vanishing. In addition to Conv blocks, YOLOv11n also includes multiple C3K2 blocks, which replace the C2f blocks used in YOLOv8, optimizing the cross-stage partial (CSP) design, thereby reducing computational redundancy in YOLOv11n, as shown in the 'd' block in Figure 1. The C3K2 blocks provide a more computationally efficient implementation of CSP. The final two modules in the backbone are spatial pyramid pooling fast (SPPF) and cross-stage partial with spatial attention (C2 PSA). The SPPF module utilizes multiple max-pooling layers (as shown in Figure 1e) to efficiently extract multi-scale  
Figure 1: Structure of YOLOv11n.  
features from the input image. On the other hand, as depicted in the 'f' block in Figure 1, the C2 PSA module incorporates an attention mechanism to enhance the model's accuracy.  
The second major structure of YOLOv11n is the neck. The neck consists of multiple Conv layers, C3K2 blocks, Concat operations, and upsampling blocks, leveraging the advantages of the C2 PSA mechanism. The primary function of the neck is to aggregate features at different scales and pass them to the head structure.  
The final structure of YOLOv11n is the head, a crucial module responsible for generating prediction results. It determines object categories, calculates objectness scores, and accurately predicts the bounding boxes of identified objects. It is worth noting that YOLOv11n, being the smallest model in the YOLOv11 series, has only one detect layer, while the YOLOv11s, YOLOv11m, YOLOv11l, and YOLOv11x models all feature three detect layers.  
From the structure of YOLOv11n, it is evident that YOLOv11n achieves efficient multi-scale feature extraction and fusion by optimizing the design of the backbone, neck, and head. Its structural highlights include the C3K2 blocks, the SPPF module, and the C2 PSA attention mechanism. Through a size-specific model pruning strategy, YOLOv11n significantly enhances the resource efficiency while maintaining high accuracy. In this paper, YOLOv11n is used to extract not only chips but also workpieces. Moreover, the predicted location of the workpiece is used to improve the results of chip detection.