High resolution underwater object detection network based on multi-scale attention feature fusion

Qing Huang; Shanlin Sun; Boyu Zhang

doi:10.1117/12.3013258

29 November 2023 High resolution underwater object detection network based on multi-scale attention feature fusion

Qing Huang, Shanlin Sun, Boyu Zhang

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370A (2023) https://doi.org/10.1117/12.3013258
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Underwater object detection is a challenging task due to issues caused by the complex underwater environment, including high similarity between backgrounds and objects, large variance in object scales, and serious blurring of objects. These issues lead to poor performance of conventional object detection methods in underwater scenes. To address these difficulties, we propose a high-resolution underwater object detection network based on multi-scale attentional feature fusion. First, HRNet is utilized to extract high-resolution features while preserving target details. Second, a multi-scale attentional fusion module is designed to effectively integrate multi-scale information in channel and spatial dimensions. Finally, DIoU loss is adopted to improve prediction box regression accuracy, and data augmentation is used to enhance network robustness. Experimental results demonstrate that compared to other object detection methods, our approach achieves better detection performance.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qing Huang, Shanlin Sun, and Boyu Zhang "High resolution underwater object detection network based on multi-scale attention feature fusion", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370A (29 November 2023); https://doi.org/10.1117/12.3013258

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KEYWORDS

Object detection

Feature fusion

Data modeling

Feature extraction

Deep learning

Target detection

Head

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