W-ESDETR: An Efficient Scale-Aware Transformer Detection Network for Inland Waterway Surveillance

Visual ship surveillance in inland waterways remains challenging because complex shoreline backgrounds and adverse weather make it difficult to balance real-time speed with precise localization. To address this problem, we propose W-ESDETR (Wavelet-Efficient Scale-aware DETR), an efficient Transformer-based detection network built on RT-DETRv2. An Enhanced Feature-correlation with Large Selective-kernel (EFLS) module is employed in the hybrid encoder to reduce background interference, while a Wavelet-enhanced Multi-scale Dense Feature Fusion Network (W-MDFFN) with an Affinity Parametric Wavelet Downsampling (APWD) operator preserves high-frequency texture details for distinguishing visually similar ship classes. In addition, the EB-Inner-MPDIoU loss, adapted from the YOLO-TSL formulation, is adopted to improve bounding-box localization under blurred vessel contours. Experimental results show that W-ESDETR achieves 99.18% mAP@50 on SeaShips and 95.2% mAP@50 on our self-constructed inland dataset at 173.4 FPS, with 8.8% fewer parameters than RT-DETRv2-R18. It also increases mAP@50:95 from 78.2% to 79.6% on the self-constructed dataset. These results indicate that W-ESDETR improves detection accuracy over the baseline while maintaining comparable real-time inference efficiency, providing a high-precision, low-latency solution for intelligent inland waterway surveillance.