## 1 Introduction  
The global shipbuilding industry, as a crucial supporting sector for international trade, is undergoing transformation and upgrading toward intelligent manufacturing. According to statistics from the United Nations Conference on Trade and Development, over 80% of global trade goods are transported by maritime shipping, making the maritime industry a vital pillar of international trade. In this context, the level of intelligence in the shipbuilding industry [1] directly relates to the efficiency and cost control of global maritime logistics. However, due to the unique characteristics of order-based production mode in shipbuilding, the sorting work after ship component cutting has become a key bottleneck, constraining production efficiency improvement. Ship component sorting, as the core process connecting cutting and assembly, directly affects the overall production cycle [2]. Current sorting modes rely on Quick Response (QR) code recognition technology to achieve semi-automated operation but still require manual classification and stacking according to patterns, with heavy dependence on manual processes severely constraining the transition toward fully intelligent shipbuilding. Unlike standardized manufacturing industries, ship component manufacturing features diverse patterns, complex and varied geometric shapes, different size specifications, and widespread component stacking occlusion phenomena, limiting the application effectiveness of traditional intelligent recognition technologies. For target recognition and classification tasks in industrial scenarios, existing research is primarily based on deep learning methods. Single-stage detection networks represented by the You Only Look Once (YOLO) series have gained widespread attention in industrial applications due to their efficiency but typically require large amounts of training data to achieve ideal recognition performance. When facing complex scenarios in ship component sorting with limited dataset samples and target stacking occlusion, existing methods still encounter problems such as inaccurate recognition and missed detection. To address the problems of small samples and target occlusion, researchers mainly adopt two improvement approaches. The first is network structure improvement design, such as Residual Network (ResNet) and Densely Connected Convolutional Network (DenseNet), using residual connections and dense connectivity, respectively, achieving lightweight through improved connection methods but with limited effectiveness in handling stacking occlusion problems [3]. The second is the introduction of attention mechanisms, which although showing potential in detection performance improvement, existing attention mechanisms still face key challenges in industrial scenarios of ship component recognition including high computational complexity, insufficient small-sample adaptability, and limited capability for processing spatial redundant information [4]. Current attention mechanism research demonstrates promising results in general computer vision tasks, yet effective solutions remain needed for the specific requirements of ship component sorting, including small sample learning, target stacking recognition, and feature redundancy suppression. In ship manufacturing environments, existing attention mechanisms encounter difficulties distinguishing between diverse component patterns and complex geometric shapes, while manual markings and surface scratches create interference that conventional spatial attention cannot effectively filter, and component stacking occlusion produces spatial relationships that current channel attention mechanisms fail to properly address. Therefore, a Triplet Spatial Reconstruction Attention (TSA) mechanism is proposed, addressing these limitations through threshold-based feature separation combined with triplet parallel processing to simultaneously achieve high accuracy in small-sample scenarios while maintaining computational efficiency for industrial deployment.