PJ-YOLO: Prior-Knowledge and Joint-Feature-Extraction Based YOLO for Infrared Ship Detection

• Published in: Journal of marine science and engineering Vol. 13; no. 2; p. 226
• Main Authors: Liu, Yongjie, Li, Chaofeng, Fu, Guanghua
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2025
• Subjects: Accuracy; Algorithms; Brightness distribution; Datasets; Deep learning; Design; Feature extraction; Formability; Image resolution; Information processing; infrared image; Infrared imagery; joint feature extraction; Knowledge; Liu, Timothy; Localization; Modules; prior knowledge; Radiation; Semantics; ship detection; Shipping industry; Ships; Wavelet transforms
• DOI: 10.3390/jmse13020226
• ISSN: 2077-1312

Infrared ship images have low resolution and limited recognizable features, especially for small targets, leading to low accuracy and poor generalization of traditional detection methods. To address this, we design a prior knowledge auxiliary loss for leveraging the unique brightness distribution of infrared ship images, we construct a joint feature extraction module that sufficiently captures context awareness, channel differentiation, and global information, and then we propose a prior-knowledge- and joint-feature-extraction-based YOLO (PJ-YOLO) for use in detecting infrared ships. Additionally, a residual deformable attention module is designed to integrate multi-scale information, enhancing detail capture. Experimental results on the SFISD and InfiRray Ships datasets demonstrate that the proposed PJ-YOLO achieves state-of-the-art detection performance for infrared ship targets. In particular, PJ-YOLO achieves improvements of 1.6%, 5.0%, and 2.8% in mAP50, mAP75, and mAP50:95 on the SFISD dataset, respectively.