基于改进YOLOX的无人机航拍图像密集小目标车辆检测

doi:10.13229/j.cnki.jdxbgxb.20230779

Abstract

Abstract:

Aiming at the issues of severe missed detections and low detection accuracy for small targets in the perspective of drone aerial photography， an improved YOLOX network is proposed for the detection of drone aerial images. To enhance the feature learning ability of the network， the ASFF module is introduced in the feature fusion part， and the CA mechanism is embedded in the neck of the network. To enhance the network's learning of positive samples， the binary cross-entropy loss function is replaced with the varifocal loss function. Experimental results show that the improved YOLOX network has better detection efficiency， and its mAP_@50 reaches 91.50% and mAP_{@50_95} reached 79.65%. The visualization results in various traffic scenarios show that compared with other algorithms， the optimized network has a lower missed detection rate and higher detection accuracy， which can be competent for the detection task of small target vehicles， and can provide a reference for vehicle multi-target tracking applications from a high-altitude perspective.

Key words: engineering of communications and transportation system, small target vehicle detection, loss function, coordinate attention mechanism, adaptive spatial feature fusion, YOLOX

CLC Number:

U491

He-shan ZHANG,Meng-wei FAN,Xin TAN,Zhan-ji ZHENG,Li-ming KOU,Jin XU. Dense small object vehicle detection in UAV aerial images using improved YOLOX[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(4): 1307-1318.

Figures/Tables 16

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Table 1

Fig.5

Fig.6

Table 2

Evaluation index of network detection performance"

指标	公式	意义
Precision	$P = T P T P + F P$	在所有检测到的目标中检测到正确的概率
Recall	$R = T P T P + F N$	在所有阳性样本中正确识别的概率
F₁_score	$F 1 = 2 × R × P R + P$	准确率和召回率的调和均值
AP	$A P = ∫ P R d R$	同一召回率下精度的平均值
mAP	$m A P = 1 C ∑ K C A P K$	数据集中所有平均精度之和的平均值

Table 2

Table 3

Fig.7

Fig.8

Table 4

Fig.9

Fig.10

Table 5

Fig.11

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实验平台	型号参数
CPU	12 vCPU Intel（R） Xeon（R） Platinum 8255C CPU @2.50 GHz
GPU	GeForce RTX 3080（10 GB）
操作系统	Windows 10
框架	Pytorch
编程环境	Python

参数	值
训练轮次	200
类别	3
批次	8
初始学习率	0
动量	0.9
权重衰减	5×10^-4
检测阈值	0.35
解冻训练	30
非极大值抑制	0.65

网络	mAP_@50/%	mAP_{@50_95}/%	Parameters/M
Original YOLOX	89.3	78.44	8.94
ASFF	89.7	78.83	14.38
ASFF+CA	89.9	79.35	14.40
Improved YOLOX	91.5	79.65	14.40

网络	mAP/%	Parameters/M	Precision/%	Recall/%	F₁_score	Gflops/G
Original YOLOX	89.3	8.94	95.89	93.69	93.97	26.8
Faster R-CNN	62.8	137.00	46.20	72.47	56.33	185.1
SSD	88.6	26.29	78.38	93.75	85.33	140.9
YOLOv5	89.3	7.10	84.91	91.33	81.00	16.5
Improved YOLOX	91.5	14.40	95.33	94.94	94.13	35.2

Dense small object vehicle detection in UAV aerial images using improved YOLOX

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