GhostNetZero: AI for Detecting Marine Ghost Nets

Zhongqi Miao, Gabriele Dederer, Mareen Lee, Emrah Birsin, Crayton Fenn, Kai Krutzke, Theodoros Stougiannis, Eva Borges, Lisa Strempel, Rahul Dodhia, Juan Lavista Ferres

Abandoned, lost, or otherwise discarded fishing gears (ALDFG), commonly referred to as ghost nets, pose a persistent global threat to marine biodiversity. Constructed from durable synthetic polymers, ghost nets remain intact for decades, continuing to entangle and kill marine organisms while damaging habitats and imposing economic burdens on fisheries and coastal communities. Despite their ecological significance, ghost nets are notoriously difficult to detect due to oceanic dispersal, submersion, and burial in sediment. Side-scan sonar has emerged as a powerful detection tool, but its high cost and limited spatial coverage constrain its large-scale application. In this study, we evaluate the feasibility of applying modern computer vision and AI techniques to sonar-derived imagery for automated ghost net detection. In our experiments, we achieved an approximately 90% ghost net detection rate in data collected from the Baltic Sea and the Puget Sound regions. To operationalize this approach, we developed GhostNetZero, a human-in-the-loop web platform that integrates AI predictions with expert review, streamlining validation workflows and enabling iterative model refinement. Our results highlight the promise of AI-assisted sonar analysis in scaling ghost net detection, complementing costly manual surveys and supporting targeted removal operations. By advancing automated detection methods, this study contributes to global efforts to mitigate the impacts of ghost gear and safeguard marine biodiversity.