UWV-Yolox:用于水下视频物体检测的深度学习模型
Haixia Pan1, Jiahua Lan1, Hongqiang Wang1
1School of Software, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了UWV-Yolox,这是一种用于水下视频的增强物体检测模型. 它通过结合背景和注意力机制来提高模糊,低对比度的镜头的精度.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 机器人技术 机器人技术 机器人技术
背景情况:
- 水下视频物体检测由于视频质量差 (模糊,对比度低) 而面临挑战.
- 现有的Yolo模型与这些条件作斗争,缺乏框架对框架的上下文分析.
研究的目的:
- 开发一个改进的视频物体检测模型,UWV-Yolox,用于具有挑战性的水下环境.
- 为了提高低质量的水下视频中对象检测的准确性和稳定性.
主要方法:
- 利用对比度有限的自适应式直方体平衡用于视频增强.
- 引入了一个具有坐标注意力的CSP_CA模块,以改善对象表示.
- 开发了一种新的损失函数 (回归和丧).
- 实现了一个框架级优化模块,利用框架间的关系.
主要成果:
- 在UVODD数据集上,UWV-Yolox模型实现了89.0%的mAP@0.5.
- 与原来的Yolox模型相比,显示出3.2%的改进.
- 与其他模型相比,展现出更稳定的对象预测.
结论:
- UWV-Yolox有效地解决了现有的水下物体检测模型的局限性.
- 提出的改进,包括注意力机制和框架级优化,显著提高了性能.
- 该模型的改进可以适应其他对象检测架构.
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