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

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...

Full description

Saved in:
Bibliographic Details
Published inJournal of marine science and engineering Vol. 13; no. 2; p. 226
Main Authors Liu, Yongjie, Li, Chaofeng, Fu, Guanghua
Format Journal Article
LanguageEnglish
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
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2077-1312
2077-1312
DOI:10.3390/jmse13020226