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 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.
Date made available26 Oct 2021
PublisherWageningen University & Research
Temporal coverageOct 2019
Geographical coverageNorth Sea


  • by-catch registration
  • deep learning
  • object detection
  • object tracking
  • electronig monitoring
  • computer vision

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