Distribution-Aware Evaluation of Deep Learning Detectors for Floating-Object Detection on Water Surfaces: Small Objects, Class Imbalance, and Computational Efficiency
Year: 2026
Type: article
Abstract: Reliable computer-vision-based monitoring of floating pollutants requires detectors that can identify small objects, accommodate imbalanced pollutant categories, and operate under practical computational constraints. However, conventional aggregate detection metrics may not fully characterize model performance when the class and object-scale compositions of dataset partitions differ substantially. This study presents a distribution-aware evaluation of five representative object detectors—SSD300, RetinaNet, Faster R-CNN, YOLO26n, and RT-DETR-L—for floating-pollutant monitoring on water surfaces. Using a dataset comprising 4274 images, 8 pollutant categories, and 8606 annotated instances, we evaluated overall detection accuracy, class-wise performance, object-scale sensitivity, validation-to-test performance changes, and computational efficiency. The dataset exhibited pronounced class imbalance, with ‘bottle’ accounting for 79.70% of all instances, while small objects constituted 46.40%. The proportion of small objects differed markedly across partitions, accounting for 0.29% of validation instances and 66.19% of test instances. On the test set, RT-DETR-L achieved the highest [email protected] (75.23%) and [email protected]:0.95 (45.73%), whereas YOLO26n attained the highest macro-F1 at the predefined confidence threshold (69.64%). RT-DETR-L also achieved the highest small-object AP (32.56%), although performance remained substantially lower for small than for large objects across all detectors. All models showed higher [email protected] but lower [email protected]:0.95 on the test set than on the validation set, while their ranking by [email protected]:0.95 remained unchanged. Across three random seeds, class-balanced sampling was associated with higher observed mean macro-F1 and rare-class recall, while the observed mean mAP and small-object AP were lower than those of standard training. Given the limited number of runs and the observed between-seed variability, these differences should be interpreted descriptively rather than as statistically established effects. Under the tested configurations, YOLO26n achieved the highest throughput among the detectors evaluated at 640 × 640, whereas SSD300 achieved the highest raw throughput at its lower 300 × 300 input resolution. These findings demonstrate that floating-pollutant detector assessment benefits from jointly considering aggregate accuracy, category-level performance, object scale, partition-level distribution differences, and computational efficiency under the tested configuration.
Source: Water
DOI: 10.3390/w18212791
Authors: Jianwen Mao, Shibiao Fang, Wenrong Tu
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225627386