A novel approach for wild fish monitoring at aquaculture sites wild fish presence analysis using computer vision

• Published in: Aquaculture Environment Interactions Vol. 14; pp. 97 - 112
• Main Authors: Banno, Kana, Kaland, Håvard, Crescitelli, Alberto Maximiliano, Tuene, Stig Atle, Aas, Grete Hansen, Gansel, Lars Christian
• Format: Journal Article
• Language: English
• Published: Oldendorf Inter-Research Science Center 01.01.2022; Ecology Institute; Inter-Research
• Subjects: Abundance; Aquaculture; Artificial intelligence; Automation; Cages; Cameras; Computer vision; Ecological aggregations; Fish; Fish culture; Fish detection; Fish farms; Fisheries; Freshwater fishes; Monitoring; Object recognition; Procedures; Salmon; Site selection; Thresholds
• ISSN: 1869-215X; 1869-7534
• DOI: 10.3354/aei00432

Aquaculture in open sea-cages attracts large numbers of wild fish. Such aggregations may have various impacts on farmed and wild fish, the environment, fish farming, and fisheries activities. Therefore, it is important to understand the patterns and amount of wild fish aggregations at aquaculture sites. In recent years, the use of artificial intelligence (AI) for automated detection of fish has seen major advancements, and this technology can be applied to wild fish abundance monitoring. We present a monitoring procedure that uses a combination of multiple cameras and automatic fish detection by AI. Wild fish in images collected around commercial salmon cages in Norway were automatically identified and counted by a system based on the realtime object detector framework YOLOv4, and the results were compared with manual human counts. Overall, the automatic system resulted in higher fish numbers than the manual counts. The performance of the system was satisfactory regarding false negatives (i.e. non-detected fish), while the false positive (i.e. objects wrongly detected as fish) rate was above 7%, which was considered an acceptable limit of error in comparison with the manual counts. The main causes of false positives were confusing backgrounds and mismatches between detection thresholds for automated and manual counts. However, these issues can be overcome by using training images that represent real scenarios (i.e. various backgrounds and fish densities) and setting proper detection thresholds. We present here a procedure with great potential for autonomous monitoring of wild fish abundance at aquaculture sites.