鱼声纳图像识别算法基于改进的YOLOv5图像识别算法
Bowen Xing1, Min Sun1, Minyang Ding2
1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
这项研究使用增强的YOLOv5算法改进了鱼声纳图像检测,提高了可持续渔业管理的准确性. 新方法有效地识别鱼类种群,有助于海洋资源评估和保护工作.
科学领域:
- 海洋生物学 海洋生物学
- 计算机视觉 计算机视觉
- 渔业 科学 渔业 科学
背景情况:
- 可持续的渔业管理依赖于准确的鱼类库存评估.
- 过度捕捞和深海评估的挑战需要先进的检测方法.
- 声纳图像噪声会降低鱼的目标特征,降低物体检测精度.
研究的目的:
- 引入基于YOLOv5.5的改进的鱼声纳图像检测算法.
- 在杂的声纳图像中增强特征提取和小目标检测.
- 提高海洋渔业鱼类种群评估的准确性和效率.
主要方法:
- 将一个C3N模块与深度可分离的卷积和反向瓶层集成到YOLOv5.5中.
- 引入了一个小写的浅特征层,以增强更大的像素的提取.
- 结合正常化加权距离与交叉在欧盟 (IoU) 和取代非最大抑制 (NMS) 软-NMS.
主要成果:
- 改进的YOLOv5模型在精度 (2.3%),回忆 (4.7%) 和平均平均精度 (2.7%) 方面比原始模型显著提高.
- 与YOLOv3相比,增强型模型在精度 (2.5%),回忆 (6.3%) 和平均精度 (6.7%) 中取得了更大的改进.
- 该方法有效地提高了声纳图像检测的准确性,特别是对于小型和重叠的目标.
结论:
- 增强的YOLOv5算法显著提高了鱼声纳图像检测准确度.
- 这种方法为鱼类种群评估和海洋资源评估提供了一个有前途的工具.
- 无人驾驶水下车辆的进步可以利用这项技术来改善渔业管理和鱼类养殖决策.
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