PRISM: Priority-Guided Scanning in the Wavelet Domain for UAV Maritime Small Object Detection

Sparse object detection remains difficult when weak target evidence occupies only a few pixels and shares local statistics with surrounding clutter. This failure is not merely due to limited model capacity, but to a representation bottleneck: spatial domain features mix target edges, clutter textures, and scene context before they can be reliably separated. We propose PRISM, a wavelet domain detector that treats this problem as frequency separated representation learning followed by priority ordered feature interaction. PRISM decomposes each scene into directional high frequency evidence and global low frequency context, models the two streams with dedicated encoders, and fuses them through a learned content adaptive causal scan that lets likely target regions guide feature interaction before clutter dominated regions participate. Multi scale features are then reconstructed by inverse wavelet synthesis with adaptive gates and passed to a standard detection head. On low altitude UAV maritime benchmarks, including SeaDronesSee and AFO, PRISM achieves state-of-the-art performance with the largest gains on the smallest targets. Without architectural modification, it also transfers to aerial urban imagery on VisDrone, suggesting that priority ordered fusion of directional frequency evidence is a generalizable design principle for sparse visual signal detection.