Electronic monitoring (EM) systems using onboard cameras are increasingly common in global fisheries. Artificial intelligence (AI) and machine learning (ML) tools are now being trialled to analyse EM footage, with the potential to deliver faster, more accurate identification and quantification of catch. This project explores the use of AI for catch detection in Australia's sub-Antarctic fisheries, including those around Heard Island and McDonald Islands (HIMI), Macquarie Island, and the Ross Sea, where Patagonian toothfish (Dissostichus eleginoides) is the primary target.
We present preliminary results from AI models trained to detect and count both target and bycatch species such as grenadiers (Macrouridae), deepwater skates (Bathyraja spp.), and morid cods (Antimora rostrata). The HIMI fishery, which operates using longlines and holds Marine Stewardship Council (MSC) certification, provides a rigorous testing ground for these technologies. Our aim is to evaluate the feasibility of real-time, species-specific catch analysis, and length estimation, which could significantly enhance fishery monitoring while reducing costs for both industry and regulators. Challenges in training models, handling varied visual conditions, and ensuring accuracy across species are also discussed.