IoMB: evaluating object detectors on occluded and imbalanced seabird populations

Computer vision techniques are seeing increasing use in wildlife monitoring surveys but challenges remain when applying standard techniques to complex, real-world data. To gauge the state-of-the-art on this problem, we present a challenging new dataset specifically designed to reflect the challenges of monitoring cliff-nesting seabird species. Unlike the widely used benchmarks of CUB (Caltech-UCSD Birds-200-2011) (Wah et al., 2011) and NAB (North America Birds) (Horn et al., 2015), our dataset captures a wider range of relative bird sizes, a high degree of class imbalance and significant levels of occlusion. We evaluate the performance of state-of-the-art object detectors YOLO (Khanam and Hussain, 2024), RetinaNet (Lin et al., 2020), Faster R-CNN (Ren et al., 2015) and DINO (Zhang et al., 2022) on this dataset and show that these standard detectors struggle with occlusion, limited species representation, and large size variation. Although pre-training on larger datasets provides some improvement, it cannot fully address these challenges. Our study highlights the need for the development of new techniques tailored to these specific issues in wildlife monitoring.