MTD-YOLOv5:在YOLOv5模型中通过多尺度特征融合增强海洋目标检测
W E I Lian-Suo1, Huang Shen-Hao2, Ma Long-Yu2
1School of information engineering, Suqian University, SuQian, jiangsu 223800, China.
Heliyon
|February 23, 2024
概括
我们开发了MTD-YOLOv5,这是一种通过增强图像对比度和特征来改进水下海洋目标检测的新方法. 这种方法显著提高了精度和平均精度 (mAP),使水下物体识别更加清晰.
科学领域:
- 计算机视觉 计算机视觉
- 海洋生物学 海洋生物学
- 图像处理 图像处理
背景情况:
- 水下光衰减降低了图像对比度,降低了目标可见性.
- 由于多尺度目标,聚类和颗粒噪声,海上目标的检测很困难.
研究的目的:
- 改进在具有挑战性的水下条件下进行海上目标检测.
- 引入一个增强的YOLOv5模型用于水下物体识别.
主要方法:
- 拟议的MTD-YOLOv5包含灰度等级和色彩空间转换用于对比度和色彩校正.
- 引入了PCBR模块,用于有效的特征提取,以及用于捕获远程依赖的多尺度感知混合聚合 (MHP) 模块.
主要成果:
- 在标记野生鱼类数据集上,MTD-YOLOv5获得了88.1%的精度和49.6%的mAP[0.5:.95].
- 与原来的YOLOv5.5相比,其精度提高了2.6%,mAP提高了0.4%.
结论:
- MTD-YOLOv5有效地解决了水下图像退化和检测挑战.
- 拟议的模型为海洋目标检测应用提供了增强的性能.
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