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基于BES-YOLO的侧扫描声纳图像中的多尺度海洋物体检测

Quanhong Ma¹, Shaohua Jin¹, Gang Bian¹

¹Department of Oceanography and Hydrography, Dalian Naval Academy, Dalian 116018, China.

Sensors (Basel, Switzerland)

|July 27, 2024

概括

这项研究引入了BES-YOLO,用于在杂的声纳图像中改进多尺度海底目标检测. 新型网络提高了水下物体识别的准确性和效率.

科学领域:

海洋技术海洋技术
人工智能的人工智能是人工智能.
图像处理图像处理

背景情况:

侧扫描声纳图像由于高噪音和复杂的纹理,对多尺度目标检测提出了挑战.
现有的方法通常在这些苛刻的水下环境中难以准确.

研究的目的:

开发一个准确和高效的模型,用于在侧扫描声纳图像中多个尺度的海底目标检测.
在杂,复杂的水下图像中增强深度学习模型的稳定性和性能.

主要方法:

提出了BES-YOLO网络,将高效的多尺度注意力 (EMA) 机制和双向特征金字塔网络 (Bifpn) 集成到YOLOv8架构中.
实现了一个Shape_IoU损失函数,用于持续的模型优化.
使用2D离散波段分解和重建预处理数据集,以提高网络稳定性.

主要成果:

使用BES-YOLO网络实现了92.4% (mAP@0.5) 和67.7% (mAP@0.5:0.95) 的平均精度.
与YOLOv8n模型相比显著改善,mAP@0.5和mAP@0.5:0.95分别增加了5.3%和4.4%.
预处理技术提高了网络的稳定性.

结论:

在具有挑战性的侧扫描声纳图像中,BES-YOLO网络有效地提高了多尺度目标的检测精度和效率.
该模型提供了一个可行的解决方案,用于像自动水下车辆 (AUV) 等平台上的智能海底目标检测.

关键词:

这是一个YOLO YOLO.深度学习是一种深度学习.多个尺度的多个尺度对象检测检测对象检测对象检测侧扫描声纳侧扫描声纳

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