RPT-Fusion: A Time-Lag-Aware Quality-Adaptive Radar–Camera Fusion Framework for Water-Surface Object Detection

Water-surface object detection remains challenging under strong reflections, wave disturbances, adverse weather, low illumination, and distant or small targets, which can severely degrade or obscure discriminative visual cues. Although radar–camera fusion provides complementary geometric and motion information, sparse and uncertain radar observations may introduce unreliable cues, while camera–radar temporal asynchrony can further reduce cross-modal spatial consistency. To address these issues, this paper proposes Radar Prior and Time-Lag-Aware Quality-Adaptive Fusion (RPT-Fusion), a radar–camera fusion framework that preserves the camera as the primary semantic modality while treating radar as a reliability-controlled auxiliary source. Sparse 4D radar measurements are transformed into probabilistic occupancy, density, velocity, and reliability priors, with direction-dependent spatial uncertainty represented through anisotropic radar-prior modeling. Local, global, and temporal quality estimates are then jointly used to regulate radar contributions during multi-scale feature fusion. In particular, the measured camera–radar time lag is explicitly incorporated into both radar-prior construction and feature-level reliability control. Experiments on WaterScenes show that RPT-Fusion achieves 92.31% mAP50 at an Intersection-over-Union (IoU) threshold of 0.50 and 68.23% mAP50–95 averaged over IoU thresholds from 0.50 to 0.95, outperforming WS-DETR by 0.82 and 3.69 percentage points, respectively. Ablation experiments verify the contributions of radar-prior modeling, quality-adaptive fusion, and temporal quality, while repeated-training experiments show low performance variability. Time-lag analysis reveals condition-dependent benefits, with the largest observed improvement of 1.15 percentage points occurring when the camera–radar offset reaches or exceeds 20 ms. Radar-frame-dropping experiments further show that mAP50 decreases from 92.31% to 91.54% when radar observations are progressively removed, indicating that the camera-centric pathway retains a substantial detection capability under incomplete radar observations. These results demonstrate the effectiveness of reliability-controlled radar assistance for robust radar–camera object detection in complex water-surface environments.