Deep Learning based enhanced aerial object detection
DOI:
https://doi.org/10.58825/jog.2025.19.1.197Keywords:
Object detection, UAV, Super resolutionAbstract
In congested urban environments, accurate detection and counting of humans and vehicles provide valuable insights for optimizing traffic flow, identifying congestion hotspots, and designing efficient transportation systems. By leveraging computer vision algorithms, such as deep learning based object detection models, real-time monitoring of pedestrian and vehicular traffic can be achieved with high accuracy and granularity. The ability to precisely quantify pedestrian and vehicle movements enables urban planners and policymakers to make data-driven decisions regarding infrastructure development, road maintenance, and public transit planning. In this work, we enhanced the existing deep learning based network architecture for object detection using UAV images. The enhanced network architecture can detect and give a count of the number of objects for any particular area in the image.
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