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The implementation and fully compliance of the Common Fisheries Policy (CFP) of the EU depends largely on the ability to quantify total catches on board commercial fishing vessels. To this aim, the use of electronic devices is gaining relevance and vision-based electronic monitoring technologies have emerged as a more cost-effective and efficient way to monitor fishing activity. In this work, we present the iObserver 2.0, a device that uses Deep Learning image recognition to automatically identify and quantify in real time the entire catch on board fishing vessels. It builds upon two previous prototypes, improving image quality by using line scan technology. Two neural networks are used for fish species segmentation, identification, and length regression tasks. As main results of this disruptive technology, the iObserver 2.0 distinguishes more than twice the number of species than previous version, works with area scan and line scan camera images, and it is evaluated with a test set incorporating more complex images. An experimental fishing survey has been conducted to assess the system’s performance in real-life conditions, showing promising results in terms of total catch registration of target and discard fish species

Monitoring plays a key role in all aspects of fsheries management, including those related to sustainable management of resources, the economic performance of the fshery, and the distribution of benefts from the exploitation of the fshery and environment. In this work, software improvements made on the remote electronic monitoring (REM) device iObserver are described towards the improvement of fsheries monitoring by precisely identifying and quantifying fshing catches on board commercial vessel´s. To this aim, we exploit deep learning and convolutional neural networks (CNNs) capabilities and potential