Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images
- URL: http://arxiv.org/abs/2203.10194v1
- Date: Fri, 18 Mar 2022 23:51:09 GMT
- Title: Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images
- Authors: Aryaman Singh Samyal, Akshatha K R, Soham Hans, Karunakar A K, Satish
Shenoy B
- Abstract summary: Our work shows the adaptation of the popular YOLOv4 framework for predicting the objects and their locations in aerial images.
The trained model resulted in a mean average precision (mAP) of 45.64% with an inference speed reaching 8.7 FPS on the Tesla K80 GPU.
A comparative study with several contemporary aerial object detectors proved that YOLOv4 performed better, implying a more suitable detection algorithm to incorporate on aerial platforms.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for
various computer vision tasks has paved the path for numerous opportunities to
make them more effective and valuable. Object detection in aerial images is
challenging due to variations in appearance, pose, and scale. Autonomous aerial
flight systems with their inherited limited memory and computational power
demand accurate and computationally efficient detection algorithms for
real-time applications. Our work shows the adaptation of the popular YOLOv4
framework for predicting the objects and their locations in aerial images with
high accuracy and inference speed. We utilized transfer learning for faster
convergence of the model on the VisDrone DET aerial object detection dataset.
The trained model resulted in a mean average precision (mAP) of 45.64% with an
inference speed reaching 8.7 FPS on the Tesla K80 GPU and was highly accurate
in detecting truncated and occluded objects. We experimentally evaluated the
impact of varying network resolution sizes and training epochs on the
performance. A comparative study with several contemporary aerial object
detectors proved that YOLOv4 performed better, implying a more suitable
detection algorithm to incorporate on aerial platforms.
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