Hyper Look-Ahead Network for Remote Sensing Object Detection
Object detection in remote sensing images is a crucial but challenging research issue in computer vision. Compared to high-resolution images, low-resolution images of the same size typically cover a wider area and thus facilitate efficient object detection. However, the limited visual information and difficulty in distinguishing objects from the background make accurate object detection and localization more challenging. Current object detection networks struggle to adequately extract feature layer information from the feature pyramid in remote sensing images, resulting in unsatisfactory detection performance. To overcome these challenges, we propose a hyper look-ahead network, which incorporates a look-ahead structure (LS), conspicuous feature supplement attention (CFSA), and multiscale feature information process module (MFIPM). The intuition is that the CFSA is integrated into the backbone, enabling the network to rapidly locate objects and effectively ignore continuous blank areas. In addition, by incorporating the LS and the MFIPM in the neck, we enhance the information extraction from different scale feature layers and supplement small object information in deeper features. Experiments on Levir-Ship and VisDrone demonstrate the effectiveness and efficiency of the proposed method. Our method achieves 79.1 mAP with 159 FPS on Levir-Ship, which outperforms many state-of-the-art object detection methods. The code is available online.