Adaptive Spectrum-Aware Feature Disentangled Network for Small Object Detection

Small Object Detection (SOD) is a fundamental yet chal-lenging problem in computer vision due to its limited spatial resolu-tion and weak visual cues. Although recent approaches have achievedremarkable advances, the background distractors in different frequencyspectra still degrade the performance. In this paper, we propose a novelsmall object detection framework termed SFDNet, which is capableof detecting small objects via efficient spectrum-aware feature disentan-glement. Specifically, we propose an Adaptive Spectrum Disentangle-ment (ASD) module that decomposes backbone features into multiplecomplementary spectral components, aiming to construct discriminativeobject-relevant representations by discarding the background distrac-tors for each component. Afterwards, to strengthen the semantic con-sistency of the similar objects in the same class, we propose a Class-Wise Prototype Distillation (CPD) procedure, which establishes classprototypes for the object instances and enforces the compact repre-sentation by efficient prototype distillation. Extensive experiments onmultiple challenging benchmarks show that SFDNet outperforms exist-ing state-of-the-art methods by a large margin. Our code is available athttps://github.com/ManOfStory/SFDNet.