Spatial Enhanced-SSD for Multiclass Object Detection in Remote Sensing Images

Accurate multiclass object detection in remote sensing images is a challenging task, especially for small objects. since the scales of objects in remote sensing images have a great variance, almost all of the advanced detection methods have shortcomings. Consequently, improving the accuracy of multiclass objects detection has always been the direction of researchers' efforts. In this paper, a spatial enhanced-Single Shot MultiBox Detector (SE-SSD) is proposed. First, to enhance the spatial information, we enlarge the input image channels with embedding oriented-gradients feature maps. Second, the multiple output layers in the backbone network are changed to reduce one pooling operation. Finally, we design a context module to enhance the receptive field for feature layer description in SE-SSD framework. Experimental results on DOTA dataset demonstrate that Spatial Enhanced-SSD method reaches a much higher mean average precision (mAP) than Faster R-CNN, SSD and other classic detection network.