From Blurry to Brilliant Detection: YOLOv5-Based Aerial Object Detection
  with Super Resolution
Ragib Amin Nihal Benjamin Yen Katsutoshi Itoyama Kazuhiro Nakadai
The demand for accurate object detection in aerial imagery has surged with
the widespread use of drones and satellite technology. Traditional object
detection models, trained on datasets biased towards large objects, struggle to
perform optimally in aerial scenarios where small, densely clustered objects
are prevalent. To address this challenge, we present an innovative approach
that combines super-resolution and an adapted lightweight YOLOv5 architecture.
We employ a range of datasets, including VisDrone-2023, SeaDroneSee, VEDAI, and
NWPU VHR-10, to evaluate our model's performance. Our Super Resolved YOLOv5
architecture features Transformer encoder blocks, allowing the model to capture
global context and context information, leading to improved detection results,
especially in high-density, occluded conditions. This lightweight model not
only delivers improved accuracy but also ensures efficient resource
utilization, making it well-suited for real-time applications. Our experimental
results demonstrate the model's superior performance in detecting small and
densely clustered objects, underlining the significance of dataset choice and
architectural adaptation for this specific task. In particular, the method
achieves 52.5% mAP on VisDrone, exceeding top prior works. This approach
promises to significantly advance object detection in aerial imagery,
contributing to more accurate and reliable results in a variety of real-world
applications.