EISD-YOLO: Efficient Infrared Ship Detection with Param-Reduced PEMA Block and Dynamic Task Alignment
Year: 2025
Type: article
Abstract: Ship detection is critical for maritime traffic management, as infrared imaging in complex marine environments encounters significant challenges, such as strong background interference and weak target features. Thus, we propose EISD-YOLO (Efficient Infrared Ship Detection-YOLO), a high-performance lightweight algorithm specifically designed for infrared ship detection. The algorithm aims to improve detection accuracy while simultaneously reducing model parameters and enhancing computational efficiency. It integrates three core architectural innovations: first, we optimized the backbone C3k2 module by replacing the traditional bottleneck with a PEMA block to significantly reduce the parameter count; second, we integrated a lightweight DS_ADNet module, using depth-wise separable convolution to reduce parameters and alleviate computational load while maintaining robust feature representation; and third, we adopted the DyTAHead detection head, which integrates classification and localization features through dynamic task alignment, thereby achieving robust performance in complex infrared ship detection scenarios. The experimental results on the IRShip dataset demonstrate that, compared with YOLOv11n, EISD-YOLO reduced the parameters by 48.83%, while [email protected], precision, and recall all increased by 1.2%. This breaks the traditional rule that lightweight models inevitably lead to reduced accuracy. Additionally, the model size reduced from 10.1 MB to 5.7 MB, which highlights its enhanced computational efficiency and practical applicability in maritime deployment scenarios.
Source: Photonics
Authors: Siyu Wang, Yunsong Feng, Huifeng Tao, Juan Chen, Wei Jin, Liping Liu, Changqi Zhou
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415452308