Detection of Small Objects from UAV Imagery via an Improved Swin Transformer

Automated detection of small objects such as vehicles in images of complex urban environments taken by unmanned aerial vehicles (UAVs) is one of the most challenging tasks in computer vision and remote sensing communities. Convolutional neural networks (CNNs)-based deep learning models have been widely used to automatically detect objects in UAV images given their high performance. However, their detection accuracy is still unsatisfactory, particularly when it comes to small objects, due to the shortcomings of CNNs. Therefore, in this study, we propose a Swin Transformer-based model that incorporates convolutions with the Swin Transformer to extract more local information, mitigating the problem of small object detection from complex backgrounds in UAV images and further improving the detection accuracy. By using the Swin Transformer, our model leverages both the local feature extraction of convolutions and the global feature modeling of transformers. The framework comprises two primary modules: a Local Context Enhancement (LCE) module and a Residual U-Feature Pyramid Network (RSU-FPN) module. Additionally, it incorporates a loss function that combines L1 loss with Normalized Gaussian Wasserstein Distance. Our experimental results obtained on the UAV Detection and Tracking (UAVDT) dataset indicated that our proposed method increased the average precision (AP) by 21.6%, 22.3% and 25.5% over Cascade Region-based CNN (R-CNN), Faster Region based CNN (R-CNN) with ResNet-50 and with Pyramid Vision Transformer (PVT) B0, and Dynamic R-CNN detectors, respectively, indicating its effectiveness and reliability on small object detection from UAV images.