Vehicle Detection in Aerial Images
This work addresses vehicle detection for aerial imagery applications, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles vehicle detection in aerial images, which is challenging due to small vehicle size and complex backgrounds, by proposing a double focal loss convolutional neural network (DFL-CNN) that outperforms baselines on benchmark datasets.
The detection of vehicles in aerial images is widely applied in many applications. Comparing with object detection in the ground view images, vehicle detection in aerial images remains a challenging problem because of small vehicle size, monotone appearance and the complex background. In this paper, we propose a novel double focal loss convolutional neural network framework (DFL-CNN). In the proposed framework, the skip connection is used in the CNN structure to enhance the feature learning. Also, the focal loss function is used to substitute for conventional cross entropy loss function in both of the region proposed network and the final classifier. We further introduce the first large-scale vehicle detection dataset ITCVD with ground truth annotations for all the vehicles in the scene. We demonstrate the performance of our model on the existing benchmark DLR 3K dataset as well as the ITCVD dataset. The experimental results show that our DFL-CNN outperforms the baselines on vehicle detection.