UAVAI-YOLO: dense small target detection algorithm based on UAV aerial images

HE Zhiqian; CAO Lijie

doi:10.11959/j.issn.2096-6652.202422

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UAVAI-YOLO: dense small target detection algorithm based on UAV aerial images

Papers and Reports | 更新时间：2024-08-06

- UAVAI-YOLO: dense small target detection algorithm based on UAV aerial images
- Chinese Journal of Intelligent Science and Technology Vol. 6, Issue 2, Pages: 262-271(2024)
- 作者机构：
  
  1.大连海洋大学信息工程学院，辽宁大连 116023
  2.辽宁省海洋信息技术重点实验室，辽宁大连 116023
- 作者简介：
- 基金信息：
  
  Liaoning Province Department of Education Reasearch Fund(LJKZ0731)
- DOI：10.11959/j.issn.2096-6652.202422
  CLC： TP391
- Received：10 April 2024，
  
  Revised：2024-05-21，
  
  Published：15 June 2024
- 稿件说明：
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何植仟,曹立杰.UAVAI-YOLO：无人机航拍图像的小目标检测模型[J].智能科学与技术学报,2024,06(02):262-271.

HE Zhiqian,CAO Lijie.UAVAI-YOLO: dense small target detection algorithm based on UAV aerial images[J].Chinese Journal of Intelligent Science and Technology,2024,06(02):262-271.
何植仟,曹立杰.UAVAI-YOLO：无人机航拍图像的小目标检测模型[J].智能科学与技术学报,2024,06(02):262-271. DOI： 10.11959/j.issn.2096-6652.202422.

HE Zhiqian,CAO Lijie.UAVAI-YOLO: dense small target detection algorithm based on UAV aerial images[J].Chinese Journal of Intelligent Science and Technology,2024,06(02):262-271. DOI： 10.11959/j.issn.2096-6652.202422.

摘要

针对无人机航拍图像目标检测效果差的问题，提出改进的UAVAI-YOLO模型。首先，为使模型获得更加丰富的语义信息，使用改进可变形卷积网络（deformable convolutional networks，DCN）替换原骨干（backbone）网络部分通道到像素（channel-to-pixel，C2f）模块原始卷积。其次，为增加P2特征层而不增加模型参数量，提出Conv_C模块将骨干网络输出通道降维，同时避免通道降维导致的语义信息丢失，使用改进ODConv卷积替换颈部（neck）部分C2f模块原始卷积。然后，为充分利用上下文语义信息，引入双向特征金字塔网络（bidirectional feature pyramid network，BIFPN）。最后使用Wise-IoU替换原始损失函数，提高模型目标检测框的准确性。在公开的VisDrone2019数据集和UAVDT数据集的实验结果表明，UAVAI-YOLO模型相比于原YOLOv8n模型mAP@0.5分别提升了4.4%和1.1%。与其他主流目标检测模型相比具有较高的检测性精度。

Abstract

An improved UAVAI-YOLO model was proposed to address the problem of poor target detection in UAV aerial images. Firstly

in order to obtain richer semantic information for the model

the original convolution of the C2f module of the original backbone part was replaced with the improved DCN convolution. Secondly

in order to increase the P2 feature layer without increasing the number of model parameters

the Conv_C module was proposed to downscale the output channel of the backbone network

and at the same time

because of avoiding the loss of semantic information due to channel downsizing

the original convolution of the C2f module in the neck part was replaced by the improved ODConv dynamic convolution. Then

the BIFPN module was introduced to make full use of the contextual semantic information. Finally

Wise-IoU was used to replace the original loss function to improve the accuracy of the model target detection frame. Experimental results on the publicly available VisDrone2019 dataset and UAVDT dataset showed that the UAVAI-YOLO model improves 4.4% and 1.1% compared to the original YOLOv8n model mAP0.5

respectively

high detectability accuracy compared to other mainstream object detection models.

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