# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 5 Lightweight Identification Model for Shoveling Position
### Experimental Results and Analysis
#### 5.3.2 Improved YOLOv5s Model Ablation Test Analysis  
We introduce the Ghost Net network as the backbone network and integrate the RFB module into it to increase the receptive field and promote multi-scale feature fusion. The neck part of YOLOv5s also incorporates C3-CBAM to suppress complex background interference and enhance recognition accuracy. Simultaneously, we modify the loss function to EIoU, which reduces the shoveling position error of the excavation target and enhances recognition accuracy. We improve the detection accuracy of the lightweight model to make it more suitable for deployment on mobile devices. We conduct ablation experiments on various improvements to the YOLOv5s model. We maintain consistency in the number of iterations, training batch size, and other experiment conditions. Table 8 displays the experimental comparison results. The ablation experiment comparison shows that on the gravel pile detection dataset, G-v5s (Ghost Net + YOLOv5s) has a lot fewer parameters and GFlops than the original YOLOv5s. However, the F1 value and [EMAIL_ADDRESS] have gone up by 0.2 and 0.7 percentage points, respectively. This shows that the introduction of the combination of Ghost Net and YOLOv5s has produced a synergistic effect. From a classification point of view, the higher F1 value is because Ghost Net reorganized and optimized the features, which makes the features of different types of targets easier to tell apart in the feature space. From a positioning point of view, the model is better at figuring out where the target is within its bounding box. The reason for the improvement in [EMAIL_ADDRESS] is that Ghost Net enhances the model's ability to understand and utilize target information, thereby improving the detection accuracy of different categories of targets. The F1 value and [EMAIL_ADDRESS] of GB-v5s (Ghost Net + RFB + YOLOv5s) are 95.2% and 97.9%, respectively. When compared to the G-v5s model with the same weight, the [EMAIL_ADDRESS] increases by 0.3 percentage points, while the F1 value decreases by 0.1 percentage points. This shows that the rise in [EMAIL_ADDRESS] is because adding RFB makes the receptive field bigger. This lets the model get more contextual information, which helps it figure out the target's location and category more accurately, especially when the scene changes a lot. The decrease in the F1 value could potentially be attributed to the model misinterpreting some information that is similar to the target, leading to its decrease. On this basis, based on this, GBC-v5s (Ghost Net + RFB + C3-CBAM + YOLOv5s) shows a slight decrease in GFlops, an increase in F1 value of 0.4 percentage points, and a decrease in [EMAIL_ADDRESS] of 0.1 percentage points compared to GB-v5s. When the F1 value goes up, the C3-CBAM module can better focus on the image's important features and block out distracting information. This makes it easier for the model to tell the difference between the target and lowers the number of mistakes and missed opportunities. The decrease in [EMAIL_ADDRESS] is due to the fact that while the C3-CBAM module enhances the features, the way it selects or weights them affects the model's accurate judgment of the target boundary to a certain extent. Therefore, we replace the loss function CIoU with EIoU, EIoU can give the model more accurate feedback by