The statistics of harvested fish are key indicators for marine resource management and sustainability. Electronic monitoring systems (EMSs) are used to record the fishing practices of vessels in recent years. The statistics of the harvested fish in the EMS videos are manually read and recorded later by operators in data centres. However, this manual recording is time consuming and labour intensive. This study proposed an automatic approach for prescreening harvested fish in the EMS videos using convolutional neural networks (CNNs). In this study, harvested fish in the frames of the EMS videos were detected and segmented from the background at the pixel level using mask regional-based CNN (mask R-CNN). The number of the fish was determined using time thresholding and distance thresholding methods. Subsequently, the types and body lengths of the fish were determined using the confidence scores and the masks predicted by the mask R-CNN model, respectively. The trained mask R-CNN model attained a recall of 97.58% and a mean average precision of 93.51% in terms of fish detection. The proposed method for fish counting attained a recall of 93.84% and a precision of 77.31%. An overall accuracy of 98.06% was obtained for fish type identification.

中文翻译：

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使用深度卷积神经网络在电子监控系统视频中检测和计数收成鱼并确定鱼的类型

捕捞鱼的统计数据是海洋资源管理和可持续性的关键指标。电子监控系统（EMS）用于记录近年来船只的捕鱼行为。EMS视频中收割鱼的统计信息由操作员在数据中心手动读取和记录。但是，这种手动记录既费时又费力。这项研究提出了一种使用卷积神经网络（CNN）预先筛选EMS视频中收获鱼的自动方法。在这项研究中，使用基于遮罩区域的CNN（遮罩R-CNN）在EMS视频的帧中检测到了捕获的鱼并将其从像素级别的背景进行了分割。使用时间阈值和距离阈值方法确定鱼的数量。后来，分别使用置信度得分和由面罩R-CNN模型预测的面罩确定鱼的类型和体长。经过训练的口罩R-CNN模型在鱼类检测方面的召回率为97.58％，平均平均精度为93.51％。提出的鱼类计数方法召回率为93.84％，精确度为77.31％。鱼类型识别的总体准确度为98.06％。