On the use of deep learning for fish species recognition and quantification on board fishing vessels

Abstract

The development and effective compliance of efficient fishing policies that guarantee both the sustainability of marine resources and fishing activity is one of the main challenges that policymakers nowadays face. At EU level, successful implementation of the Common Fisheries Policy (CFP) depends, at a large extent, on the capacity to quantify catches on board commercial vessels. Because of the large number of fishing vessels and the high number of trips to be monitored classic, monitoring methods, mainly based on inspections, are not effective. Therefore, the use of electronic devices to quantify fishing catches is gaining relevance. The data provided by such devices, in combination with mathematical models, may be used to assess the state of the different fishing stocks and to optimize the fishing activity. In this work, we consider different algorithms based on Deep Learning (DL) for species identification and length estimation. On the one hand, for the instance segmentation task, we have adapted the Mask R-CNN algorithm to the problem of fish species identification. On the other hand, the MobileNet-V1 convolutional neural network is used for the estimation of the length of each individual. The results show that, when overlapping among individuals is moderate to low, both the identification and length estimation models are able to satisfactorily quantify the catch. In situations where overlapping among individuals is large, results need further improvements.