SpatialIE: Towards adaptive floating waste detection in unpredictable weather

Accurate detection and subsequent cleanup of floating waste are critical for ecosystem protection. However, existing methods face significant challenges in dealing with unpredictable weather, limiting their generalization capabilities. This paper proposes a novel plug-and-play architecture called Image Enhancer with Spatial Search and Aggregation (SpatialIE) . This architecture includes a novel encoder block (KANsformer) based on KANs and a dynamic decoder module leveraging prompt-based learning. The KANsformer in the encoder enhances the model’s ability to learn complex non-linear relationships in degraded images. At the same time, the Multi-Order-Based Prompt Block in the decoder enables dynamic optimization of feature processing strategies. These components allow SpatialIE to refine detection strategies to accommodate unknown weather conditions adaptively. We integrate SpatialIE with common YOLO detectors in an end-to-end training framework using the FloW-img and Water Surface Object Detection Dataset (WSODD) datasets. Experimental results show that our method delivers outstanding performance across multiple and single degradation scenarios, achieving an optimal balance between detection accuracy and storage efficiency. • Propose SpatialIE an adaptable plug-and-play image enhancer for adverse weather detection. • Introduce KANsformer encoder for robust feature learning in degraded images. • Utilize Prompt-based Multi-Order decoder for dynamic optimization under varying conditions. • Validate SpatialIE on floating waste dataset superior in multi and single degradation scenarios. • Demonstrate effectiveness with YOLO models enhancing performance and storage efficiency.