YOLO-DA: An Efficient YOLO-Based Detector for Remote Sensing Object Detection

In the past few decades, many efficient object detectors have been proposed for natural scene image object detection (OD). However, due to the complex scenes and high interclass similarity of optical remote sensing (RS) images, applying these detectors to optical RS images directly is not very effective. Most of the recent detectors pursue higher accuracy while ignoring the balance between detection accuracy and speed, which hinders the practical application of these detectors, especially in embedded devices. To meet these challenges, a fast and accurate detector based on you only look once (YOLO) with decoupled attention head (YOLO-DA) is proposed, which effectively improves detection performance while only introducing minimal complexity. Specifically, an attention module at the end of the detector is designed for guiding a neural network to extract more efficient features from the complex background while also minimizing the amount of additional computation. Moreover, a lightweight decoupled detection head with enhanced classification and localization capability is developed to detect objects with high interclass similarity. In the experiments, the proposed method effectively solves the problem of high interclass similarity and improves the mean average precision (mAP) by 6.8% on the fine-grained optical RS dataset SIMD, compared with YOLOv5-L. In addition, the proposed method improves the mAP by 1.0%, 1.7%, and 0.6% on the other three publicly open optical RS datasets, respectively. Experimental results on detection accuracy and inference time demonstrate that our method achieves the best trade-off between detection performance and speed.