An integrated end-to-end deep neural network for automated detection of discarded fish species and their weight estimation

Abstract

Abstract Sustainable management of aquatic resources requires efficient acquisition and processing of vast amounts of information to check the compliance of fishing activities with the regulations. Recent implementation of the European Common Fisheries Policy Landing Obligation implies the declaration of all listed species and sizes at the harbour. To comply with such regulation, fishers need to collect and store all discards onboard the vessel, which results in additional processing time, labour demands, and costs. In this study, we presented a system that allowed image-based documentation of discards on the conveyor belt. We presented a novel integrated end-to-end simultaneous detection and weight prediction pipeline based on the state-of-the-art deep convolutional neural network. The performance of the network was evaluated per species and under different occlusion levels. The resulting model was able to detect discards with a macro F1-score of $94.10\%$ and a weighted F1-score of $93.88\%$. Weight of the fish could be predicted with mean absolute error, mean absolute percentage error, and root squared error of 29.74 (g), $23.78\%$, and 44.69 (g), respectively. Additionally, we presented a new dataset containing images of fish, which, unlike common object detection datasets, also contains weight measurements and occlusion level per individual fish.