Vehicle Detection with Bottom Enhanced RetinaNet in Aerial Images

Peng Gao, Jinwen Tian, Yuan Tai, Tianming Zhao, Qian Gao

Abstract: Vehicle detection is one of the hot topics in lane detection and vehicle counting. Many works have been done on it and some data sets with satellite images and aerial images are proposed. However, the ratio of the vehicle targets to the background is small and the detection results are unsatisfied. In this paper, a bottom enhanced RetinaNet model named En-RetinaNet is proposed to get better performance on vehicle detection. The EnRetinaNet includes an enhanced feature pyramid network(FPN) and a bottom-top fusion before the region proposal network. Enhanced feature pyramid network adds a bottom layer to the feature pyramid network to exploit more local features. Bottom-top fusion is utilized to get a better fusion of the bottom layers and top layers. In order to get a high ratio of the objects to the images, we take a sliding window mechanism on the testing images. We discuss the effects of the training input crop size on the final results and choose a moderate size of the training input. With all the above work done, we get an improvement on the UCAS—AOD data set in contrast to the RetinaNet.