SRAF-Net: Shape Robust Anchor-Free Network for Garbage Dumps in Remote Sensing Imagery
Xian Sun, Yingfei Liu, Zhiyuan Yan, Peijin Wang, Wenhui Diao, Kun Fu
Abstract
The detection of garbage dumps is of great significance for environmental protection. Recently, deep learning algorithms have brought impressive improvements for regular object detection. Different from conventional objects, garbage dumps are more inconspicuous and irregular and have the problem of blurred boundaries. To solve these problems, we propose a shape robust anchor-free network (SRAF-Net) that consists of feature extraction, multitask detection, and postprocessing. First, our network leverages the context-based deformable (CBD) module to combine context attention and deformable convolution. The contextual information obtained by context attention enables the network to focus on objects with inconspicuous appearance, while the deformable convolution enhances the feature representation. Then, we propose a multitask detection head to regress irregular garbage dumps in a more accurate and efficient way. The anchor-based methods need to define some anchors with a fixed shape. However, our detection method is anchor-free that learns the shapes of objects from training data. The detection head adaptively generates various shapes of bounding boxes with their classification confidences and localization confidences. Weighted by the localization confidences, we merge bounding boxes during postprocessing, which alleviates the blurred boundaries. In addition, we build a new public data set named garbage dumps data set (GDD) to verify the effectiveness of our method. Extensive experiments on GDD indicate that our method surpasses the existing detection methods in terms of speed and accuracy for the garbage dumps detection task.