Robust automatic net damage detection and tracking on real aquaculture environment using computer vision

Sea cage inspection is a crucial task in marine aquaculture for both controlling the quality in the fish development process and the environmental impact of these facilities. Net damage in sea cages can cause fish to escape from fish farms to the open sea, producing serious economic losses and compromising the surrounding marine ecosystem. Currently, the most sophisticated inspection processes used in the industry rely on human visual inspection of underwater videos captured with tele-operated underwater ROV (Remotely Operated Vehicles). This process is tedious, time consuming, imprecise, highly dependant on the operator level of expertise, and has low verifiability. This article presents a comprehensive algorithm for automatic net damage detection in sea cages for aquaculture oriented to on-ROV processing and real-time processing. The proposed approach takes a video stream from an on-ROV camera, segments the image frames to separate the net of the sea cage from the background, and applies noise reduction tuned for underwater conditions. Then, to perform net damage detection, the mesh net hole areas are analyzed for detecting outliers that represent potential damage. Finally, holes neighboring to the outliers are analyzed to reduce perspective errors, and a spatial-temporal criterion using tracking is applied, to reduce the chance of false positives. The algorithm was first tested using a set of public images for comparison with state-of-the-art approaches and repeatability of the tests, and then using a dataset of real ROV inspection sequences to evaluate its effectiveness in real-world scenarios. Results show that our approach presents high levels of accuracy even for adverse scenarios and is adequate for real-time processing in embedded platforms.