# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 4 Results and Analysis
### Experimental Results  
We compared the improved YOLO-SWD with recent YOLO series algorithms, including YOLOv8 [41], YOLOv9 [42], YOLOv10 [43], Faster-RCNN [44], CenterNet [45], and YOLOv11. Considering hardware limitations and inference speed, all YOLO networks were configured with similar parameter scales: YOLOv9 adopts the \"t\" structure, while other YOLO versions use the \"n\" structure. In near-shore occlusion scenarios, object detection algorithms face challenges such as densely docked ships, port facility occlusion, and weather interference. In these scenarios, ship targets are frequently occluded by structures or weather conditions or submerged in the background, significantly increasing detection difficulty. The visualization results  
Figure 7: Training mAP curves and loss function curves. are shown in Figure 8. Based on the experimental data and visualizations, we analyze the performance of each algorithm as follows:  
1. Under weather interference: For large ship categories such as CVNs (aircraft carriers), DDGs (destroyers), CGs (cruisers), and LSTs (large ships), cloud and fog occlusion often obscure parts of the ship structures, preventing models from learning complete features. As shown in Figure 8(9), YOLOv8, YOLOv10, and YOLOv11 fail to detect the cloud-occluded carrier, while YOLOv9, Faster-RCNN, and our method succeed, demonstrating our algorithm's ability to learn local features and reduce missed detections. Additionally, as shown in Figure 8(8), when cloud shapes resemble ship contours, only YOLOv11 and our method correctly identify the ship near the carrier. In summary, our algorithm achieves the best performance under weather interference, primarily due to the enhanced local edge and contextual feature extraction capabilities of the improved modules, which are absent in other algorithms. 2. In near-shore scenarios: For medium and small ship categories such as CVs (civilian vessels), DDGs, and CGs, their features are easily occluded by dock facilities, as shown in Figure 8(1,4). Notably, our method exhibits the lowest miss rate overall, outperforming YOLOv9, Faster-RCNN, and YOLOv11, which struggle when large and medium targets coexist. YOLOv8, CenterNet, and YOLOv10 perform the worst, with severe miss rates. For the CV category, due to the small size of targets and dense overlapping (over 60% overlap in docking areas), miss rates are particularly severe. As shown in Figure 8(2), CV-class ships are densely packed, occluding each other's edges. The low resolution (20-30 pixels per target) further exacerbates miss rates. All the algorithms exhibit significant miss rates, but our method, CenterNet, and YOLOv11 achieve the best overall performance, with no widespread misses, while YOLOv8 and YOLOv10 perform the worst. As shown in Figure 8(11,12), in noisy images, all the algorithms exhibited a certain degree of false positives and false negatives, with YOLOv8 performing the worst, being almost unable to detect any targets. However, thanks to the improved modules that enhance the model's ability to extract ship features more accurately, our model had no false negatives and exhibited the fewest missed detections, achieving the best performance. When port facilities dominate the interference (Figure 8(3,6,10)), our method and CenterNet achieve the best overall performance, with minimal misses despite occasional false positives. YOLOv9 and YOLOv11 follow with more misses, while YOLOv8 and YOLOv10 nearly fail to detect CV-class ships under port interference. From Table 1, the performance gaps among algorithms in the CV category are significant: YOLOv8 (Precision 68.5%/Recall 45.9%) and YOLOv9 (Precision 70.8%/Recall 47.4%) perform poorly. Faster-RCNN (Precision 78.2%/Recall 33.8%) achieves the highest precision but suffers from the lowest Recall. YOLOv10 (Precision 68.2%/Recall 47.2%) and YOLOv11 (Precision 79.6%/Recall 51.8%) show moderate improvements.