Ship detection based on the modified YOLOV5 for SAR imagery

Zhongbo Wang; Miao He; Qinghai Ding; Haibo Luo

doi:10.1117/12.3034316

13 June 2024 Ship detection based on the modified YOLOV5 for SAR imagery

Zhongbo Wang, Miao He, Qinghai Ding, Haibo Luo

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318009 (2024) https://doi.org/10.1117/12.3034316
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Synthetic aperture radar (SAR) is an essential tool for ocean surveillance. As the main participants on the ocean, ships are the most important targets for ocean monitoring. So it is of great importance to develop ship detection algorithms for SAR sea images. The algorithms based on Convolutional Neural Network perform far better than the traditional methods based on manual features on ship detection task due to the powerful feature representation abilities. The algorithms based on Convolutional Neural Network can be divided into one-stage algorithms, and two-stage algorithms. Two-stage algorithms have high accuracy, but are relatively time-consuming. One-stage algorithms have high inference speed, but compared with two-stage algorithms, they have lower accuracy. So in this article, we proposed an modified one-stage detection algorithm to improve the accuracy of ship detection in a condition that the modified algorithm meet the real-time requirement. First, the small model of one-stage algorithm YOLOV5 is chosen as the base network to get the high inference speed. Then, to improve the accuracy of ship detection with a little increase in inference time and the model parameters, we integrate the one–layer super-resolution architecture with the simplist structure into the YOLOV5 network. Finally, we conducted the comparative experiments on our Dataset to verify the performance of modified YOLO V5. The experimental results show that the modified method has obtained an Average Precision (AP) improvement than the original YOLO V5 for detecting ships in SAR images with a little increase in inference time and the model parameters.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhongbo Wang, Miao He, Qinghai Ding, and Haibo Luo "Ship detection based on the modified YOLOV5 for SAR imagery", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318009 (13 June 2024); https://doi.org/10.1117/12.3034316

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KEYWORDS

Detection and tracking algorithms

Synthetic aperture radar

Super resolution

Education and training

Network architectures

Algorithm development

Convolutional neural networks

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