  Detection of small-sized targets in aerial views is a challenging task due to
the smallness of vehicle size, complex background, and monotonic object
appearances. In this letter, we propose a one-stage vehicle detection network
(AVDNet) to robustly detect small-sized vehicles in aerial scenes. In AVDNet,
we introduced ConvRes residual blocks at multiple scales to alleviate the
problem of vanishing features for smaller objects caused because of the
inclusion of deeper convolutional layers. These residual blocks, along with
enlarged output feature map, ensure the robust representation of the salient
features for small sized objects. Furthermore, we proposed a recurrent-feature
aware visualization (RFAV) technique to analyze the network behavior. We also
created a new airborne image data set (ABD) by annotating 1396 new objects in
79 aerial images for our experiments. The effectiveness of AVDNet is validated
on VEDAI, DLR- 3K, DOTA, and the combined (VEDAI, DLR-3K, DOTA, and ABD) data
set. Experimental results demonstrate the significant performance improvement
of the proposed method over state-of-the-art detection techniques in terms of
mAP, computation, and space complexity.
