基于改进YOLO和迁移学习的水下鱼类目标实时检测.pdf

摘摇 要摇 为了实现非限制环境中水下机器人基于视频图像的水下鱼类目标快速检测,提出基于改进 YOLO 和迁移

学习的水下鱼类目标实时检测算法. 针对 YOLO 网络的不足,设计适合水下机器人嵌入式系统计算能力的精简

DOI摇 10. 16451 / j. cnki. issn1003鄄6059. 201903001摇 摇 摇 摇 摇 摇 中图法分类号摇 TP 181

ABSTRACT 摇 To fast detect underwater fish in unrestricted underwater environment based on

underwater video collected by underwater robots, a real鄄time detection algorithm for underwater fish based

on improved you only look once(YOLO) and transfer learning is proposed. Firstly, an underwater鄄YOLO

for the embedding computer system of underwater robots is designed to overcome the shortcomings of

underwater fish in unconstrained underwater environment. Compared with the traditional YOLO, the

proposed underwater鄄YOLO generates better detection performance in complex scenes with small fish and

overlapped fishes.

Key Words摇 Convolutional Neural Network, Deep Learning, Fish Target Detection, You Only Look