## 6 Conclusions  
Computer vision technology based on deep learning has been widely used in the industrial field. Although existing algorithms can meet the basic needs of many practical applications, their performance can be further improved by optimizing existing models. Based on the analysis of the current competitive one-stage detection algorithms (YOLOv5, YOLOv7 and YOLOv8), this paper focuses on the network structure upgrade of the YOLOv5, which has good accuracy and fast inference speed. We propose the FSS-YOLO model, which aims to enhance the real-time inspection capability of a fastener screw maintenance robot, which can only offer limited computing resources. The C3 Fast module is proposed to substitute for the C3 module in the backbone for the model's efficiency promotion. This modification results in the computation speed increasing while the P reduces. In our approach, the SIoU instead of the original CIoU is utilized. The SIoU introduces the concept of the vector angle and redefines the distance loss, which effectively reduces the degrees of freedom in the regression. This leads to the faster convergence of the network and improves the regression accuracy, allowing the  
Figure 8: The results are compared with those of other algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Params** & **FLOPs (G)** & **P** & **mAP@50** & **Recall** & **F1 Score** & **FPS** \\ \hline YOLOv5n & 1760518 & 4.1 & 0.819 & 0.635 & 0.556 & 0.662 & 176 \\ YOLOv7-tiny & 6007596 & 13.0 & 0.859 & 0.59 & 0.533 & 0.657 & 170 \\ YOLOv8n & 3005843 & 8.1 & 0.829 & 0.631 & **0.675** & 0.716 & 166 \\
**FSS-YOLO (Ours)** & **1452982** & **3.3** & **0.906** & **0.676** & 0.631 & **0.743** & **189** \\ \hline \hline YOLOv5 & YOLOv7-tiny & YOLOv8 & Ours & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Quantitative indicators of the YOLO series. model to efficiently identify fastener screws. Additionally, we integrate the SA attention mechanism (Shuffle Attention) in the sampling process of the neck network. The shuffling and reorganization of input features effectively enhances the feature expression ability of fastener screws. Aiming at addressing the issue of insufficient datasets of fastener screws, a new dataset of fastener screws based on various environments is built. The data enhancement method is used to expand the samples, and images with different processing degrees are randomly selected to simulate the images collected by the robot in different weathers, which can effectively avoid the similarity of the image features and improve the generalization ability of the model. The experiment result shows that our work is 7.3% faster in terms of the FPS than the baseline model, while reducing by 17.4% and 19.5% the Params and FLOPs, and the P, mAP@50, Recall, and F1 scores are improved by 10.6%, 6.4%, 13.4%, and 12.2%. Extensive evaluations demonstrate the superiority of the proposed model when compared with some competitive models. Meanwhile, the fastener screw maintenance robot obtains a substantial advancement in its competency for the fastener screw detection task. At present, the fastener screw maintenance robot developed based on FSS-YOLO has conducted more than 200 practical experiments and is about to be used for disassembly and assembly tasks concerning type II fasteners. The performance of this robot is satisfactory. In the future, we will conduct in-depth research on the adaptability of the model to improve the recognition accuracy and reduce the false detection rate in more complex actual environments, like dim conditions and partly covered by trash, so that the model can perform better, thereby improving the fastener screw detection function of the fastener screw maintenance robot. Conceptualization, Y.C., F.Z. and M.H. ; methodology, Y.C., H.Z., Q.T. and M.H. ; software, Y.C., J.X. and M.H. ; writing--original draft, M.H. ; writing--review and editing, Y.C., J.X., Q.T. and H.Z. ; funding acquisition, Y.C., H.Z. and F.Z.