SIAM-WFEF: Small target tracking network based on wavelet pooling layer and frequency band enhancement and fusion

Unmanned aerial vehicles (UAVs) are increasingly used for target tracking, but long-distance/high-altitude imaging often yields tiny, blurred targets with few pixels. Conventional feature extraction struggles to capture local details and discriminative cues, making targets vulnerable to background interference and reducing tracking accuracy. This paper proposes Siam-WFEF, a Siamese network-based tracker featuring two synergistic modules: Wavelet Pooling Layer (WPL) and Frequency Band Enhancement and Fusion (FBEF). WPL replaces traditional pooling by applying the Discrete Wavelet Transform to decompose feature maps into low- and high-frequency sub-bands, preserving critical details of small targets. FBEF then refines each sub-band and adaptively fuses the enhanced frequency features, jointly suppressing background clutter while amplifying target cues. Extensive experiments on UAV123, DTB70, and VisDrone2019-SOT demonstrate that Siam-WFEF achieves significant performance gains over mainstream trackers, particularly for small targets under challenging conditions.