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Increased necessity to monitor vital fish habitat has resulted in proliferation of camera-based observation methods and advancements in camera and processing technology. Automated image analysis through computer vision algorithms has emerged as a tool for fisheries to address big data needs, reduce human intervention, lower costs, and improve timeliness. Models have been developed in this study with the goal to implement such automated image analysis for commercially important Gulf of Mexico fish species and habitats. Further, this study proposes adapting comparative otolith aging methods and metrics for gauging model performance by comparing automated counts to validation set counts in addition to traditional metrics used to gauge AI/ML model performance (such as mean average precision - mAP). To evaluate model performance we calculated percent of stations matching ground-truthed counts, ratios of false-positive/negative detections, and coefficient of variation (CV) for each species over a range of filtered outputs using model generated confidence thresholds (CTs) for each detected and classified fish. Model performance generally improved with increased annotations per species, and false-positive detections were greatly reduced with a second iteration of model training. For all species and model combinations, false-positives were easily identified and removed by increasing the CT to classify more restrictively. Issues with occluded fish images and reduced performance were most prevalent for schooling species, whereas for other species lack of training data was likely limiting. For 23 of the examined species, only 7 achieved a CV less than 25%. Thus, for most species, improvements to the training library will be needed and next steps will include a queried learning approach to bring balance to the models and focus during training. Importantly, for select species such as Red Snapper (Lutjanus campechanus) current models are sufficiently precise to begin utilization to filter videos for automated, versus fully manual processing. The adaption of the otolith aging QA/QC process for this process is a first step towards giving researchers the ability to track model performance through time, thereby giving researchers who engage with the models, raw data, and derived products confidence in analyses and resultant management decisions. |
