Abstract Accurate identification of river sewage outfalls is crucial for effective water pollution control. Unmanned Aerial Vehicles (UAVs), with their high mobility and wide coverage, have become a vital tool for this monitoring task. However, this application is hampered by the dual challenges of robust multi-scale object detection and lightweight model deployment on computationally limited platforms. To address the trade-off between accuracy and efficiency, this study proposes an efficient deep learning-based detection method, termed Scale-Oriented Detection Network (SODNet). Specifically, we propose an Efficient Context Feature Pyramid Network (ECFPN) to enhance multi-scale feature representation. Additionally, a shared decoupled head with a Multi-Scale Grouped Fusion (MSGF) module strengthens feature extraction while reducing computational costs. Furthermore, a channel pruning strategy is employed to compress the model, notably improving inference speed. Experimental results demonstrate that SODNet achieves an AP@50 of 89.9% and a precision of 91.1%, representing improvements of 1.2% and 2.7% over the baseline model, respectively. Meanwhile, parameters and GFLOPs are reduced by 77.5% and 73.6%. On a deployed edge device, SODNet achieves 40.3 FPS. These findings indicate that SODNet gains substantial computational efficiency while maintaining excellent detection performance, making it ideal for resource-constrained UAV scenarios and offering a feasible solution for intelligent environmental supervision.
Zeng et al. (Thu,) studied this question.