Automating the in-line sorting, fish stock statistics extraction, and quality grading of fish through real-time computer vision (

Quality control in the food sector is becoming increasingly important, driven by growing demands for food safety and traceability, as well as the need for fair pricing for producers. Consequently, there is a strong focus on automating this process, with the aim of moving from sample-based checks to individual product inspections. For fishery products, however, this is not straightforward, given that they are often difficult to handle, there is a wide variety of species, and the quality characteristics are not easy to assess (e.g. smell, texture).
The FishEyeQ project is conducting research into advanced image analysis techniques to achieve this, using Artificial Intelligence. Research is being conducted to determine which combination of sensors and algorithms enables a rapid, accurate and objective assessment of fish quality and the identification of potential defects. The data obtained (e.g. volume) are also valuable for faster monitoring of commercial fish stocks and, consequently, sustainable fisheries.