From video to encounter histories: individual identification and machine vision for salmonid capture-recapture monitoring
Individual encounter histories are central to capture-recapture models, but fisheries monitoring is often reduced to counts that do not account for variation in detection probability. This study presents a machine-vision workflow for converting long-term video surveillance of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) spawning runs into individual-level data for capture-recapture analysis. The workflow detects fish-positive frames, saves video clips, extracts fish-head regions of interest, and organizes cropped images for re-identification. A binary EfficientNetB0 fish detector achieved validation accuracy of 0.9918 and PR AUC of 0.9992; at a conservative threshold of 0.98, validation false positives were eliminated while retaining 94.3% recall. A YOLOv8n model localized fish-head regions, and an EfficientNetB0 ArcFace model trained on head images from 700 identities achieved 99.80% accuracy among accepted known matches and an image-weighted false-accept rate of 0.81% when non-training identities were treated as unknown. Zero-shot closed-set retrieval achieved 99.17% top-1 accuracy across 1,416 identities excluded from model training, demonstrating strong generalization to previously unseen identities.