## 5 Discussion  
To satisfy the fast detection requirement of the fastener screw maintenance robot, we choose the YOLOv5m model with the smallest Params and lowest computation cost as the baseline. Although YOLOv5m and YOLOv5s perform well in terms of the P, mAP, Recall, and F1 scores, their network Params and FLOPs are too large, resulting in a huge computation resource expenditure during operation. This means that these two models are too slow to complete the real-time recognition job on devices with low capability. Then, different IoU loss functions are adopted, and the results are shown in Table 4. It shows that the SIOU loss function can achieve the best FPS with higher overall performance. Therefore, the YOLOv5n with SIOU is used. \begin{table}
\begin{tabular}{c c} \hline \hline
**Configuration** & **Version** \\ \hline System & Windows 11 \\ CPU & Intel Core i5-12500I1 @3.10 GHz \\ GPU & NVIDIA GeForce RTX 3060 \\ Language & Python 3.8 \\ Acceleration & CUDA 12.0 \\ Framework & PyTorch 1.13.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental environment: Parameter configuration. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Details** \\ \hline Optimization algorithm & SGD \\ Learning rate & 0.01 \\ Batch size & 8 \\ Img size & \(640\times 640\) \\ Epochs & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter configuration. Table 5 lists the light-weight modifications using MobileNetv3, GhostNet, and C3 Fast. The Params and FLOPs are significantly reduced, but the P, mAP@50, Recall, and F1 scores are decreased simultaneously. The C3 Fast achieves the best score within the three modules, while its Params and FLOPs are acceptable. A great enhancement in the FPS appears due to the hardware latency optimization ability of the FasterNet Block-improved C3 Fast module. Therefore, C3 Fast is used for the light-weight upgradation. For higher detection accuracy, an attention mechanism is introduced. As Table 6 shows, barely any difference appears between the Params and FLOPs. The SA wins in terms of the P, mAP@50, and F1, while there is only a slight lack in Recall. The combination of channel-wise attention and spatial-wise attention has superior performance in this fastener screw detection task. Thus, the adopted modules for performance enhancement are the C3 Fast, SloU, and SA. We compare the metrics for each modification stage, and the results are presented in Table 7. C3 Fast significantly improves the computational speed, but the P, mAP@50, Recall, and F1 scores appear to diminish. Then, SloU and SA are adopted to compensate for the loss in the P, mAP@50, Recall, and F1 scores. To quantitatively evaluate the overall performance of the FSS-YOLO, several light-weight YOLO series models are trained and tested on fastener screw datasets. As shown in Table 8, FSS-YOLO exhibits significantly improved performance in all the aspects compared to the baseline. The mAP@50 of FSS-YOLO is 14.5% higher than that of YOLOv7-Tiny and the F1 score is 13% higher. The mAP@50 of FSS-YOLO is 7.1% higher than that of YOLOv8 and the F1 score is 3.7% higher. In general, compared with YOLOv5n, YOLOv7-tiny, and YOLOv8n, our work's performance is the best. The FSS-YOLO not only has the smallest Params and FLOPs but also improves the detection accuracy, which means that it is more conducive to completing the detection task of the fastener screw maintenance robot. The actual test results are shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Params** & **FLOPs (G)** & **P** & **mAP@50** & **Recall** & **F1 Score** & **FPS** \\ \hline YOLOv5n+C3 Fast+SloU+SEAttention & 1461078 & 3.4 & 0.853 & 0.624 & 0.573 & 0.685 & 166 \\ YOLOv5n+C3 Fast+SloU+SimAM & **1452886** & **3.3** & 0.803 & 0.59 & **0.634** & 0.708 & 185 \\
**YOLOv5n+C3 Fast+SloU+SA** & 1452982 & **3.3** & **0.906** & **0.676** & 0.631 & **0.743** & **189** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the performance of various attention mechanisms.