Visual perception for long-distance and small target detection in autonomous maritime navigation

Zhang, R, Ji, X, Loughney, S, Wang, J and Yang, Z (2025) Visual perception for long-distance and small target detection in autonomous maritime navigation. Ocean Engineering, 332. ISSN 0029-8018

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Abstract

In the pursuit of advancing autonomous maritime navigation, this study aimed to develop a novel architecture designed to enhance the detection accuracy of distant and small targets under the constraints of real-time performance and robustness. Through the innovative integration of the Convolutional Block Attention Module (CBAM) into the detection model's backbone, the study achieved superior feature extraction capabilities tailored for the complexities of maritime environments. Further optimization of the Spatial Pyramid Pooling (SPP) module ensured model compactness and computational efficiency, vital for deployment on edge devices. A key methodological novelty lay in the incorporation of the S-IoU loss function, which offers superior bounding box regression accuracy over the traditional Generalized Intersection over Union, directly contributing to more precise navigation and effective obstacle avoidance. The proposed enhancements collectively yielded a 5.1 % increase in mAP@50 %, accompanied by an 11.2 % reduction in model parameters and a 12.6 % decrease in computational complexity (GFLOPs). These findings underscore the potential of the presented architecture to significantly contribute to maritime safety, presenting an optimized solution for collision avoidance and navigation assistance in congested sea routes and adverse weather conditions.

Item Type:	Article
Uncontrolled Keywords:	4012 Fluid Mechanics and Thermal Engineering; 4005 Civil Engineering; 4015 Maritime Engineering; 40 Engineering; 7 Affordable and Clean Energy; 0405 Oceanography; 0905 Civil Engineering; 0911 Maritime Engineering; Civil Engineering; 4005 Civil engineering; 4012 Fluid mechanics and thermal engineering; 4015 Maritime engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) V Naval Science > VM Naval architecture. Shipbuilding. Marine engineering
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	1 May 2025
Date of first compliant Open Access:	13 June 2025
Date Deposited:	13 Jun 2025 13:59
Last Modified:	13 Jun 2025 14:15
DOI or ID number:	10.1016/j.oceaneng.2025.121447
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26588

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