Chang Chin-Chun, Wang Yen-Po, Cheng Shyi-Chyi
Department of Computer Science and Engineering, National Taiwan Ocean University, 2, Pei-Ning Rd., Keelung 202301, Taiwan.
Sensors (Basel). 2021 Nov 17;21(22):7625. doi: 10.3390/s21227625.
Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide "standardized" feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.
成像声纳系统被广泛用于监测浑浊或低环境光水域中的鱼类行为。为了分析声纳图像中的鱼类行为，通常需要进行鱼类分割。本文采用Mask R-CNN对声纳图像中的鱼类进行分割。从不同浅水区获取的声纳图像在鱼类与背景之间的对比度上可能有很大差异。这种差异可能导致在一个养鱼场收集的示例上训练的Mask R-CNN对其他养鱼场的鱼类分割无效。本文提出了一种预处理卷积神经网络（PreCNN），为Mask R-CNN提供“标准化”特征图，并便于将为一个养鱼场训练的Mask R-CNN应用于其他养鱼场。PreCNN旨在将鱼类实例的学习与鱼类养殖环境的学习解耦。PreCNN是一个语义分割网络，并与条件随机场集成。PreCNN可以利用连续的声纳图像，并可以通过半监督学习进行训练以利用未标记的信息。实验结果表明，基于PreCNN输出的Mask R-CNN比直接在声纳图像上的Mask R-CNN更准确。将为一个养鱼场训练的Mask R-CNN加PreCNN应用于新的养鱼场也更有效。
