Using semi-supervised learning to detect beluga whales from aerial image sequences

IntroductionPrecise population monitoring of beluga whales (Delphinapterus leucas) is crucial for Arctic conservation management; yet, the manual annotation of aerial drone imagery is costly and logistically challenging. Calf detection is particularly important because calf presence and survival rates are indicators of reproductive health, recruitment success, and long-term population viability. However, calves are challenging detection targets due to their smaller size, lower frequency, and greater variability in appearance compared with adults. Although deep learning-based object detection offers considerable potential for automated wildlife monitoring, supervised approaches typically require large quantities of manually annotated data, limiting their application in conservation programs with restricted annotation resources.MethodsThis study systematically evaluated semi-supervised marine mammal detection from aerial imagery by comparing SEMI-DETR, a semi-supervised detection transformer, with four variants of YOLO11 across seven annotation budgets ranging from 1% to 50% of labeled data. Experiments were conducted using a bespoke dataset of 7,655 high-resolution aerial drone images annotated for adult and calf classifications.ResultsSEMI-DETR achieved a mean Average Precision (mAP) of 19.2 at 0.5:0.95 using only 34 labeled images (1% annotation budget), representing a 20% relative improvement over the best-performing supervised baseline. The performance advantage peaked at +6.6 mAP points at the 5% labeling level. Semi-supervised learning particularly improved calf detection, achieving a 24.3% relative improvement at 1% labeling compared with 16.8% for adults. Overall, comparable detection performance could be achieved with a 5- to 10-fold reduction in annotation requirements without substantially compromising detection quality.DiscussionThese findings demonstrate that semi-supervised learning can substantially reduce annotation requirements for automated marine mammal detection, with particularly strong benefits for the detection of calves. Improved calf detection has direct implications for population assessment and the monitoring of reproductive success and recruitment in beluga populations. This study establishes an empirical benchmark for semi-supervised marine mammal detection and provides practical guidance for allocating limited annotation resources in wildlife monitoring and conservation applications.