Data underlying the publication: Automatic discard registration in cluttered environments using deep learning and object tracking: class imbalance, occlusion, and a comparison to human review

Data for training and evaluation of a method for detection and counting demersal fish species in complex, cluttered and occluded environments that can be installed on the <br>conveyor belts of fishing vessels. The data mainly exists of images of fish on a conveyer belt with the corresponding annotations. This was used to train a neural network (YOLOv3) to detect and classify fish species. Because each fish is visible in multiple images, the fishes were tracked over consecutive images and the total number of fish per specie was counted. These counts were compared to human review.

本数据集用于训练与评估一种可部署于渔船传送带的方法，该方法可在复杂、杂乱且存在遮挡的环境中检测并计数底栖鱼类（demersal fish）物种。数据集主要包含传送带上的鱼类图像及其对应标注信息。该数据集被用于训练神经网络（YOLOv3）以实现鱼类物种的检测与分类。由于单条鱼类会在多幅图像中出现，研究人员对连续帧图像中的鱼类进行追踪，并统计得到各物种的总个体数量，随后将该统计结果与人工审核结果进行比对。