# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 5 Discussion  
To evaluate the contributions of the three key modules, this paper designs progressive ablation experiments, with results shown in Table 2. Starting from the baseline model (original YOLOv11), modules are incrementally added (Schemes 1-3) to validate their individual and synergistic effects by comparing changes in mean Average Precision (mAP) and class-wise AP. In Scheme 1, the original attention mechanism is replaced with the dynamic local-kernel attention (DLKA) module to enhance feature selection for ship targets. The DLKA module works by dynamically adjusting the attention weights applied to local regions in the image. This allows the model to focus on the most critical regions of the image by adaptively scaling attention across different spatial locations. As a result, DLKA helps the model to more effectively capture fine-grained details of the ship structures, leading to an improvement in the detection of mid-sized ships. Table 2 shows that the mAP increases from 81.2% to 82.5%, confirming DLKA's effectiveness in focusing on crucial regions of interest. Furthermore, small-sample categories (e.g., LST and SP) show significant improvements in Recall and AP, indicating DLKA's ability to better capture features and enhance recognition for these targets. However, for other categories, there is a slight decrease in AP, which could be attributed to the model overly prioritizing small-sample classes, potentially limiting its ability to generalize to broader feature extraction. In Scheme 2, the introduction of both DLKA and CKSP results in further performance gains, with mAP rising to 83.1%. CKSP (Contextual Kernel Spatial Pooling) improves the model's ability to capture critical spatial and structural features, especially for targets that require fine structural information. The key mechanism used in CKSP is StripPooling, which operates by pooling features along specific regions while maintaining the structural integrity of the target. StripPooling preserves spatial details by aggregating information in localized \"strips\" of the feature map, ensuring that the spatial relationships within a target are well captured. This approach improves the detection of targets with complex structures, as seen in SP's AP increase from 72.6% to 79.6%. CKSP's ability to focus on spatial relationships and finer details leads to better performance for such targets. Categories that previously experienced AP declines in Scheme 1 recover slightly due to CKSP's enhanced feature extraction. However, performance fluctuates for small-sample categories (e.g., LST and CV), highlighting the challenge of balancing the preservation of local details with general feature learning. Finally, in Scheme 3, integrating all three modules (DLKA, CKSP, and WTConv) achieves the optimal performance, with mAP reaching 83.9%. WTConv (wavelet transform convolution) enhances convolutional feature extraction by introducing multi-resolution capabilities, allowing the model to capture features at multiple scales. This helps to improve detection across various target sizes, especially those with diverse spatial characteristics. The synergistic effect of DLKA, CKSP, and WTConv enables the model to perform better at