Monitoring and surveillance of urban road traffic using low altitude drone images: a deep learning approach

In the contemporary era, the global explosion of traffic has created many eye-catching concerns for policymakers. This not only enhances pollution but also leads to several road accident fatalities which may be greatly reduced by proper monitoring and surveillance. Further, with the advent of UAV technology and due to the incompatibility of traditional techniques, surveillance has become one of UAVs prominent application domains. However, it requires algorithmic analysis of aerial images which becomes extremely challenging due to multi-scale rotating objects with large aspect ratios, extremely imbalanced categories, cluttered background, and birds-eye view. Therefore, this article presents the novel aerial image traffic monitoring and surveillance algorithms based on the most advanced and popular DL object detection models (Faster-RCNN, SSD, YOLOv3, and YOLOv4) using the AU-AIR dataset. This dataset is exceedingly imbalanced and to resolve this issue, another 500 images have been grabbed by web-mining techniques. The novel contribution of this work is two-fold. First, this article scientifically distinguishes the inappropriateness of ground-view images for aerial object detection. Second, a regress comparison of these algorithms has been made to investigate their effectiveness. Extensive experimental analysis endorses the efficiency of YOLOv4 as it outperforms the other developed models by a minimum mAP margin of 88%. Also, more than 6 times high detection speed and greater adaptability with stronger detection robustness ensure its real-time practical implementation.