WDARFNet: A Wavelet-Domain Adaptive Receptive Field Network for Improved Oriented Object Detection in Remote Sensing

Oriented object detection in remote sensing images is a particularly challenging task, especially when it involves detecting tiny, densely arranged, or occluded objects. Moreover, such remote sensing images are often susceptible to noise, which significantly increases the difficulty of the task. To address these challenges, we introduce the Wavelet-Domain Adaptive Receptive Field Network (WDARFNet), a novel architecture that combines Convolutional Neural Networks (CNNs) with Discrete Wavelet Transform (DWT) to enhance feature extraction and noise robustness. WDARFNet employs DWT to decompose feature maps into four distinct frequency components. Through ablation experiments, we demonstrate that selectively combining specific high-frequency and low-frequency features enhances the network’s representational capacity. Discarding diagonal high-frequency features, which contain significant noise, further enhances the model’s noise robustness. In addition, to capture long-range contextual information and adapt to varying object sizes and occlusions, WDARFNet incorporates a selective kernel mechanism. This strategy dynamically adjusts the receptive field based on the varying shapes of objects, ensuring optimal feature extraction for diverse objects. The streamlined and efficient WDARFNet achieves state-of-the-art performance on three challenging remote sensing object detection benchmarks: DOTA-v1.0, DIOR-R, and HRSC2016.