Lightweight Underwater Marine-Debris Detection for Sustainable Ocean Monitoring Using Receptive-Field Aggregation and Residual Channel-Spatial Recalibration

Abstract

Marine debris threatens aquatic habitats and complicates inspections in ports, seabed environments, and offshore infrastructure. Previous lightweight detectors remain vulnerable to weak texture, blurred boundaries, and cluttered multi-scale features, while direct network expansion conflicts with restricted onboard resources. This study adapts YOLO11n by integrating Receptive-Field Aggregation (RFA) with a Residual Channel-Spatial Recalibration (RCSA) implementation based on dynamic residual groups. Experiments used a public 15-class dataset with 10,884 training images and 1001 model-selection validation images containing 1892 annotated objects. All principal checkpoints were trained for 100 epochs. Across seeds 42, 2026, and 3407, RFA + RCSA achieved validation precision 0.861 ± 0.016, recall 0.801 ± 0.012, mean average precision at IoU 0.5 (mAP@0.5) 0.848 ± 0.001, and mAP@0.5:0.95 0.511 ± 0.002. On an audited group-disjoint holdout (498 images; 963 instances), the corresponding means were 0.820 ± 0.033, 0.748 ± 0.014, 0.779 ± 0.018, and 0.467 ± 0.007. The detector contains 4.19 M parameters and requires 8.91 giga floating-point operations (GFLOPs). These results position it as a lightweight candidate for resource-constrained remotely operated vehicle (ROV) perception; they do not establish real-time embedded deployment.