YOFOR: Solo te enfocas en regiones de objetos para la detección de objetos diminutos en imágenes aéreas

With development of deep learning methods, performance of object detection has been greatly improved. However, the high resolution of remotely sensed images, the complexity of the background, the uneven distribution of objects, and the uneven number of objects among them lead to unsatisfactory detection results of existing detectors. Facing these challenges, we propose YOFOR (You Only Focus on Object Regions), an adaptive local sensing enhancement network. It contains three components: adaptive local sensing module, fuzzy enhancement module and class balance module. Among them, adaptive local sensing module can adaptively localize dense object regions and dynamically crop dense object regions on view, which effectively solves problem of uneven distribution of objects. Fuzzy enhancement module further enhances object region by weakening the background interference, thus improving detection performance. Class balancing module, which analyzes dataset to obtain distribution of long-tailed classes, takes into account direction of tailed classes and distance around object, and operates on tailed classes within a certain range to alleviate long-tailed class problem and further improve detection performance. All three components are unsupervised and can be easily inserted into existing networks. Extensive experiments on the VisDrone, DOTA, and AI-TOD datasets demonstrate the effectiveness and adaptability of the method.