基于Faster-rcnn的水下目标检测算法研究

Exploring Underwater Target Detection Algorithm Based on Faster-rcnn

对海洋资源开发的关键是实现对水下目标实时而准确的检测,但由于水介质的吸收以及悬浮粒子的散射作用,水下待测目标往往存在颜色失真、对比度低等复杂问题,这极不利于准确评估目标检测算法的性能。本文提出一种基于Faster-rcnn的水下目标检测算法,该算法以Faster-rcnn结构为主框架,将ResNet-101深度神经网络替代Faster-rcnn原本的VGG-16卷积神经网络作为特征提取和训练初始化的共享卷积网络,同时采用Water-Net网络对水下图像数据集进行增强处理,最后针对部分图像标签数据过少的问题采取了标签数据增强的方法。通过实验证明,数据集的增强性能有效提升检测算法的性能,且能满足实时检测的需求。

How to achieve real-time and accurate detection of underwater targets is the key to the development of marine resources.However,due to the absorption of water media and the scattering of suspended particles,underwater targets often face problems such as color distortion and low contrast,which is extremely unfavorable for evaluating the performance of the target detection algorithm.For this reason,this paper proposes an underwater target detection algorithm based on Faster-rcnn.The algorithm takes the Faster-rcnn structure as the main framework,and then replaces the original VGG-16 convolutional neural network of Faster-rcnn with the ResNet-101 deep neural network as a shared convolutional network for feature extraction and training initialization.At the same time,Water-Net is used to enhance the underwater image data set to improve the performance of the target detection algorithm.Finally,a label enhancement method is adopted to solve the problem of too few image label data.Training experiments have proved that the performance of our algorithm has a significant improvement compared with the original data,and can meet the needs of real-time detection.