Fish stock assessment often involves manual fish counting by taxonomy
specialists, which is both time-consuming and costly. We propose an automated
computer vision system that performs both taxonomic classification and fish
size estimation from images taken with a low-cost digital camera. The system
first performs object detection and segmentation using a Mask R-CNN to identify
individual fish from images containing multiple fish, possibly consisting of
different species. Then each fish species is classified and the predicted
length using separate machine learning models. These models are trained on a
dataset of 50,000 hand-annotated images containing 163 different fish species,
ranging in length from 10cm to 250cm. Evaluated on held-out test data, our
system achieves a $92\%$ intersection over union on the fish segmentation task,
a $89\%$ top-1 classification accuracy on single fish species classification,
and a $2.3$~cm mean error on the fish length estimation task.

提出了一种基于计算机视觉的自动化鱼类数量评估系统，该系统能够通过低成本数字相机拍摄的图像进行鱼类分类和尺寸估计，训练模型在手动标注的 50000 张图像数据集上训练，实现了鱼类分割、物种分类和长度估计任务的高精度。