ADN-YOLO: An improved ship detection model based on YOLOv

Existing infrared imaging techniques have garnered considerable attention and have achieved notable progress in all weather ship target detection tasks, owing to their robustness against varying ambient lighting conditions. However, due to the inherent limitations of infrared images,such as low spatial resolution and insufficient texture information the performance of multi-scale ship target detection remains suboptimal. These challenges significantly hinder the overall improvement of detection accuracy. To address this issue and enhance the detection performance of multi-scale ship targets, particularly small ones, in infrared imagery,this paper proposes an improved You Only Look Once (YOLO) based detection model named ADN-YOLO. The model first introduces a Dynamic Upsampler (Dysample) module, which more effectively integrates semantic information across different layers. This integration balances low level detailed features with high level semantic representations, thereby enhancing the model?s ability to perceive target edges and structural characteristics. Second, a lightweight downsampling module (ADown) is incorporated to reduce the parameter count while improving both the efficiency and representational capacity of feature extraction. Additionally, to address the issue of small targets being highly sensitive to localization errors, a new loss function is designed based on the Wasserstein distance. This function combines the Normalized Wasserstein Distance (NWD) with the Complete Intersection over Union (CIoU), thereby enhancing the model?s ability to accurately localize small targets. Comprehensive experimental validation is conducted on a marine infrared target detection dataset. Compared to the standard YOLOv11 model, the proposed ADN-YOLO reduces the number of parameters by 20.3%, achieves a 1.9%increase in mAP, a 1.9% boost in Recall, and lowers FLOPs by 1.1G, demonstrating its effectiveness and practicality for infrared image target detection tasks.