## 5 Discussion  
The proposed study introduces a Triplet Spatial Reconstruction Attention (TSA) mechanism integrated with YOLOv8n to address ship component detection challenges in small-sample industrial manufacturing scenarios. The approach achieves 88.7% mean Average Precision while reducing parameters to 2.6 M and maintaining linear computational complexity. This discussion examines the technical innovations compared to existing methods, practical implications for industrial deployment, potential application prospects, current limitations, and future research directions. Traditional attention mechanisms exhibit fundamental limitations when applied to industrial small-sample detection scenarios. Approaches like SCConv and CA, while effective in standard computer vision tasks, demonstrate inherent design constraints in industrial environments. SCConv's sequential processing of spatial and channel information creates computational bottlenecks that limit real-time deployment capabilities, while CA's coordinate-based attention fails to adequately address feature redundancy prevalent in cluttered manufacturing scenes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Attention** & **Precision/\%** & **mAP/\%** & **Recall/\%** & **GFLOPs** & **Parameters** \\ \hline No addition & 82 & 85.2 & 83.3 & 8.2 & 3,011,823 \\ \hline ScConv & 81.4 & 85.6 & 84.6 & 8.0 & 2,879,887 \\ \hline CA & 82 & 85.3 & 84.9 & 8.2 & 3,023,543 \\ \hline TSA & 84.2 & 88.7 & 87.1 & 7.5 & 2,625,035 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the effects of different attention mechanisms. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **mAP/\%** & **Recall/\%** \\ \hline ResNet50 & 81.4 & 84.5 & 82.2 \\ ResNet101 & 79.6 & 85.6 & 85.9 \\ YOLOv5n & 45.1 & 52.1 & 55.8 \\ YOLOv8n & 82 & 85.2 & 83.3 \\ YOLOv9t & 80.7 & 86.1 & 82.0 \\ YOLOv10n & 77.4 & 82.9 & 77.7 \\ YOLO-Ship & 84.2 & 88.7 & 87.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the effect of different models. Recent advances in object detection increasingly explore transformer-based attention mechanisms and sophisticated multi-scale architectures. While transformer-based approaches demonstrate powerful global modeling capabilities through self-attention operations, they typically require substantial computational resources and extensive training data to establish effective attention patterns, presenting challenges for resource-constrained industrial applications. Furthermore, complex multi-scale feature fusion frameworks, despite achieving impressive accuracy in general scenarios, often introduce computational overhead that may not align optimally with the geometric patterns and spatial constraints characteristic of manufacturing environments. The threshold-based separation strategy employed in TSA addresses these limitations through targeted spatial-semantic processing that enables more granular feature discrimination while maintaining computational efficiency for industrial small-sample scenarios. The TSA mechanism demonstrates the potential for extension to manufacturing contexts sharing similar characteristics with ship component detection, particularly scenarios involving geometric component classification, small-sample constraints, and computational efficiency requirements. The lightweight architecture and parallel processing design may prove applicable to manufacturing environments where edge computing deployment offers advantages. The performance improvements achieved by YOLO-Ship demonstrate practical significance in shipbuilding applications where component recognition accuracy impacts production efficiency. The 3.5% mAP improvement corresponds to meaningful cost reductions in large-scale manufacturing operations, where error minimization helps prevent material waste and rework cycles.