Complex backgrounds, significant target size variations, and severe sea clutter in maritime infrared imagery often result in missed or false detections. To address this challenge, an improved method based on YOLOv8n, termed maritime infrared target detection-YOLO (MITD-YOLO), is proposed to enhance target detection accuracy in maritime infrared images.

MITD-YOLO incorporates a diverse branch module (DBB) and enhanced multi-scale convolution (EMSConv) to leverage multi-scale convolutions, enabling the model to more effectively capture complex features. A triple attention mechanism is employed to facilitate spatial and channel-wise feature interaction, thereby improving key feature extraction. Additionally, the powerful-IoUv2 (PIoUv2) loss function is introduced to address the anchor box expansion problem, leading to improved detection accuracy and enhanced model robustness.

Experimental results show that the improved model significantly enhances the efficiency of maritime infrared target detection, with a 2.3% increase in precision and a 1.7% increase in recall. The model achieves an average precision of 88.9%, and 132.8 FPS, outperforming the original model.

MITD-YOLO enhances maritime infrared target detection performance and provides a more reliable target detection technology for applications such as maritime surveillance and ship navigation, contributing to the advancement of intelligent maritime systems.