An Anchor-Free Network With Density Map and Attention Mechanism for Multiscale Object Detection in Aerial Images

Accurate detection of the multiple classes in aerial images has become possible with the use of anchor-based object detectors. However, anchor-based object detectors place a large number of preset anchors on images and regress the target bounding box while anchor-free object detections predict the location of objects directly and avoid the carefully predefined anchor box parameters. Object detection in aerial images is faced with two main challenges: 1) the scale diversity of the geospatial objects and 2) the cluttered background in complex scenes. In this letter, to address these challenges, we present a novel Anchor-Free Network with a Density map (DM) and attention mechanism (DA2FNet). Considering the extreme density variations of the detection instances among the different categories in aerial images, the proposed DA2FNet model conducts DM estimation with image-level supervision for the geospatial object counting, to acquire global knowledge about the scale information. A simple and effective image-level global counting loss function is also introduced. In addition, a compositional attention network (AN) is further introduced to enhance the saliency of the foreground objects. The proposed DA2FNet method was compared with the state-of-the-art object detection models, achieving excellent performance on the NWPU VHR-10, RSOD, and DOTA datasets.