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Object Detection for UAV Aerial Scenarios Based on Vectorized IOU.

Shun Lu1, Hanyu Lu1,2, Jun Dong3,4

  • 1College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China.

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|March 30, 2023
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Summary

This study introduces novel methods to improve object detection in drone imagery, enhancing accuracy for small and overlapping objects. The new techniques significantly boost performance on benchmark datasets.

Keywords:
UAV aerial imagesVIOU lossYOLOv5multi-scale feature fusion networkobject detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Object detection in Unmanned Aerial Vehicle (UAV) imagery faces challenges including multi-scale objects, numerous small objects, and high overlap.
  • Existing methods struggle to effectively address these specific difficulties in aerial datasets.

Purpose of the Study:

  • To enhance the accuracy and robustness of object detection algorithms for UAV applications.
  • To overcome limitations in detecting small, multi-scale, and overlapping objects within aerial images.

Main Methods:

  • A Vectorized Intersection Over Union (VIOU) loss function was developed to improve bounding box regression accuracy.
  • A Progressive Feature Fusion Network (PFFN) was proposed to enhance semantic feature extraction and small object detection.
  • An Asymmetric Decoupled (AD) head was introduced to optimize classification and regression performance.

Main Results:

  • The proposed VIOU loss improves bounding box regression by utilizing vector-based comparisons of box dimensions and center points.
  • PFFN enhances the fusion of deep semantic information with shallow features, significantly improving small object detection in multi-scale scenes.
  • The AD head effectively separates classification and regression tasks, boosting overall network capabilities.
  • Performance improvements were observed on the VisDrone 2019 dataset (9.7% increase) and the DOTA dataset (2.1% increase) compared to YOLOv5s.

Conclusions:

  • The integrated approach of VIOU loss, PFFN, and AD head offers substantial improvements for UAV-based object detection.
  • The developed methods effectively address key challenges in aerial object detection, particularly for small and overlapping objects.