Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater vision, and then a CNN (Convolutional Neutral Network) method for solving the weakly illuminated problem for underwater images is proposed to train the mapping relationship to obtain the illumination map. After the image processing, a deep CNN method is proposed to perform the underwater detection and classification, according to the characteristics of underwater vision, two improved schemes are applied to modify the deep CNN structure. In the first scheme, a convolution kernel is used on the feature map, and then a downsampling layer is added to resize the output to equal . In the second scheme, a downsampling layer is added firstly, and then the convolution layer is inserted in the network, the result is combined with the last output to achieve the detection. Through comparison with the Fast RCNN, Faster RCNN, and the original YOLO V3, scheme 2 is verified to be better in detecting underwater objects. The detection speed is about 50 FPS (Frames per Second), and mAP (mean Average Precision) is about 90%. The program is applied in an underwater robot; the real-time detection results show that the detection and classification are accurate and fast enough to assist the robot to achieve underwater working operation.

中文翻译：

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基于深度CNN方法的水下图像处理与目标检测

由于水下勘探在深海资源的开发和利用中的重要性，为了避免危险的高压深海环境，水下自主作业变得越来越重要。对于水下自主操作，智能计算机视觉是最重要的技术。在水下环境中，作为预处理程序的弱照明和低质量图像增强对于水下视觉来说是必需的。本文将max-RGB方法与灰色阴影方法相结合来实现水下视觉的增强，然后提出了一种CNN（卷积神经网络）方法来解决水下图像的弱照明问题，以训练水下机器人的视线。映射关系以获得照度图。图像处理后 提出了一种深度CNN方法进行水下检测和分类，根据水下视觉的特点，提出了两种改进方案来对深度CNN结构进行修改。在第一种方案中， 卷积核用于 要素贴图，然后添加下采样图层以将输出大小调整为等于 。在第二种方案中，首先添加下采样层，然后将卷积层插入网络，将结果与最后的输出合并以实现检测。通过与Fast RCNN，Faster RCNN和原始YOLO V3的比较，方案2被证明在检测水下物体方面更好。检测速度约为50 FPS（每秒帧），而mAP（平均平均精度）约为90％。该程序应用于水下机器人。实时检测结果表明，检测和分类准确，快速，足以帮助机器人完成水下作业。