# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 5 Lightweight Identification Model for Shoveling Position
### Network Model and Improvements  
YOLOv5 is well regarded in practical industrial applications. The latest version of YOLO has increased the number of layers and parameters in the network architecture, resulting in a larger model size, requiring more computing resources to deploy and operate, and an increasing amount of data for training. Therefore, based on the current operational requirements for unmanned loaders, YOLOv5s was selected. It has fewer parameters and a smaller model size and can be improved to be suitable for mobile devices and scenarios with limited computing resources.  
The architecture of YOLOv5s primarily comprises three components: the input, the backbone network, and the detecting head [18]. Cross-shading and residual connections connect the backbone network to other networks. These make it easier for information to flow and for features to be used efficiently while keeping the network's depth. Furthermore, the detection head employs convolutional and pooling layers to ascertain the target's location and category information from the feature map derived by the backbone network. The network structure diagram of YOLOv5s is shown in Figure 18a.  
To make the recognition model more lightweight, reduce its size, and ensure the loader's accuracy in identifying shovel point positions, this study employs four optimization strategies based on the original model. The improved YOLOv5s model network is shown in Figure 18b. The main improvements are as follows:  
1. Ghost Net replaces the backbone network of YOLOv5s. Its \"ghost module\" uses the inherent similarity of feature maps to generate rich feature maps at low cost, reduce the amount of calculation, and achieve a lightweight model, which is convenient for running on mobile devices with limited resources, such as unmanned loaders.  
Figure 18: Network structure diagram, where: (**a**) is the original YOLOv5s network structure diagram. (**b**) is the improved YOLOv5s network structure diagram in this paper.  
Adding RFB to the Ghost Net backbone can expand the receptive field to obtain broader contextual information, which is crucial for position recognition during loader shoveling operations. Since the loader's operating environment is complex, accurate position recognition needs to consider the surrounding environment. RFB can assist the model in effectively utilizing environmental information to locate the target, while also fostering multi-scale feature fusion to accommodate the recognition of shoveling targets at varying distances.
3. We add the C3-CBAM module based on the above-improved model. The C3 module reduces the amount of calculation and enhances the learning ability. The CBAM weights the features based on channel and spatial dimensions. During the loader operation, the channel attention can highlight key feature channels related to the excavation position, while the spatial attention can suppress complex background interference and improve the recognition accuracy.
4. Changing CIoU to EIoU can make the loss function more accurate and reduce the positioning error of the operating position target, thereby improving the recognition accuracy, which is of vital importance for unmanned loaders to achieve safe production during operation.