Arild Madshaven

Norwegian University of Science and Technology

Papers

Total Citations

H-Index

About

Arild Madshaven is a researcher at the intersection of computer vision and aquaculture engineering, whose work focuses on developing automated monitoring systems for fish farming operations. His primary research areas include underwater image analysis, net cage integrity inspection, and deep learning-based object detection in challenging marine environments. Madshaven's most significant contribution is his pioneering work on hole detection in aquaculture net cages from video footage, a critical application for preventing farmed salmon escapes that threaten wild salmon populations. His 2022 paper on this topic has garnered 8 citations, establishing a foundation for non-invasive, real-time monitoring solutions that could replace costly manual inspections. Beyond this core work, Madshaven has explored the broader challenge of detecting net irregularities using state-of-the-art computer vision approaches, addressing the practical difficulties of underwater visibility, lighting variations, and complex net geometries. His research directly supports sustainable aquaculture practices by minimizing ecological risks while improving operational efficiency for fish farms. Madshaven's work represents an important step toward fully autonomous aquaculture monitoring systems, combining practical engineering challenges with cutting-edge machine learning techniques to solve real-world environmental problems.