DWT ‐ YOLO : Wavelet‐Based Detection to Enhance Sensor Resolution for Small Targets

ABSTRACT The detection of small objects in remote sensing imagery presents significant challenges, mainly attributed to intricate background complexities, inadequate feature extraction performance, and constrained receptive field dimensions. Moreover, under constrained computational resources, improving detection accuracy while reducing the model's computational complexity and parameter count has become an urgent issue. In response to these challenges, a novel lightweight model specifically designed for small object detection, named DWT‐YOLO, is proposed in this study. By introducing the Discrete Wavelet Transform (DWT) combined with convolutional down‐sampling operations, the model effectively expands the receptive field and enhances feature extraction capabilities. Simultaneously, an innovative Wavelet Convolution Fusion module (WCFM) is designed, which improves multi‐scale feature fusion and cross‐domain information association, enhancing the feature representation for small objects while keeping model complexity relatively low. Additionally, the model incorporates a Spatial‐Channel Decoupling Down‐sampling (SCDown) module, which effectively reduces redundant parameters and improves detection accuracy. We first validated the effectiveness of DWT‐YOLO on the RSOD dataset and then further assessed its small object detection performance on the VisDrone2019 dataset. The proposed model demonstrates a 5.4% enhancement in detection accuracy compared to the baseline model. The model has only 5.8M parameters, and it outperforms several advanced models in detection performance. Experimental results demonstrate that DWT‐YOLO not only achieves significant improvements in small object detection tasks but also exhibits lightweight characteristics, providing new solutions and technical insights for this field. The implementation source code of the proposed method is publicly accessible at the following repository: http://github.com/zzgithubFly/DWT‐YOLO .