# Research on Shoveling Position Analysis and Recognition of Unmanned Loaders for Gravel Piles
## 5 Lightweight Identification Model for Shoveling Position
### Experimental Results and Analysis
#### 5.3.3 Comparison of Different Object Detection Models  
To fully and accurately test how well the lightweight shoveling position identification optimization algorithm suggested in this paper worked, we performed a number of experiments where we compared it to the YOLOv5 series algorithm and some YOLOv8 algorithms. Table 9 displays the comparison results. During the experiment, we recorded and compared the key indicators of each algorithm in the shovel position recognition task in detail. The experimental results show that the proposed method performs well in the comprehensive comparison. The [EMAIL_ADDRESS] improves by 1.1% compared to YOLOv5s, 3.7% compared to YOLOv5n, 3% compared to YOLOv5m, and 1.3% compared to YOLOv8n. However, compared with YOLOv8s, the [EMAIL_ADDRESS] of this method is only 0.1% different. In addition to comparing the detection accuracy, we also consider the number of parameters, GFIops, and F1 value of the algorithm. The results show that compared with YOLOv8s, the number of parameters and GFIops of this algorithm are reduced by 6.24 M and 21.3, respectively, and the F1 value is increased by 0.6 percentage points, which means that under the same detection accuracy, our algorithm has better resource utilization efficiency. Despite slightly increasing the number of parameters compared to YOLOv5n and YOLOv8n, this algorithm successfully achieves the lightweight effect and improves the accuracy of shovel position recognition. Simultaneously, during the experiment, both the train_loss of the improved model and the validation set loss, val_loss, exhibited a downward trend throughout the iteration process, as illustrated in Figure 24. Figure shows a small gap between them. This phenomenon shows that the model did not have obvious overfitting problems during the training process. In order to fully demonstrate the performance advantages of the improved YOLOv5s model, we use Figure 25 to completely reflect this. Figure 25a shows the [EMAIL_ADDRESS] performance index of the improved model, which can more intuitively demonstrate the rationality of the improved model. The figure reveals that the improved YOLOv5s model outperforms YOLOv5s, YOLOv5n, YOLOv5m, and YOLOv8n in terms of [EMAIL_ADDRESS], closely matching the performance of YOLOv8s. In addition, the P-R curve (precision-recall curve) presented in Figure 25b is a common method for evaluating the classification performance of the model. We obtain this basic curve by sorting the prediction results of the classification model under different thresholds and then calculating the rela \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Parameter Quantity** & **GFlops** & **R** & **P** & **F1** & **[EMAIL_ADDRESS]** \\ \hline YOLOv5n & 1.8 M & 4.3 & 93.90\% & 93.80\% & 93.8\% & 94.3\% \\ YOLOv5s & 7.2 M & 16.5 & 94.90\% & 95.40\% & 95.1\% & 96.9\% \\ YOLOv5m & 21.3 M & 49.2 & 93.10\% & 94.80\% & 93.9\% & 95\% \\ YOLOv8n & 3.1 M & 8.1 & 94.20\% & 94.40\% & 94.3\% & 96.7\% \\ YOLOv8s & 11.1 M & 28.7 & 95.10\% & 95.10\% & 95.1\% & 98.1\% \\ Proposed & 4.86 M & 7.4 & 95.30\% & 96.10\% & 95.7\% & 98\% \\ method & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparison of detection effects of different models. sion (P) and the recall (R). The closer the curve is to the upper right corner, the better the model performance. From Figure 25b, we can see that the improved YOLOv5s model has comparable performance to YOLOv8s. Moreover, considering that the improved model has significantly reduced the number of parameters and GFlops, it can be determined that the improvement of the model is effective. In summary, the improved model in this paper demonstrates high performance in the shoveling position recognition task, demonstrating good detection accuracy, resource utilization efficiency, and avoiding overfitting. This model offers a viable option for unmanned loader shoveling position recognition in practical applications.