Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2611-2618.DOI: 10.11772/j.issn.1001-9081.2022091343

• Multimedia computing and computer simulation • Previous Articles    

Small target detection algorithm for train operating environment image based on improved YOLOv3

Meijia LIANG1(), Xinwu LIU2, Xiaopeng HU1,3   

  1. 1.School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu Sichuan 611756,China
    2.Data and Intelligence Technology Center,Zhuzhou CRRC Times Electric Company Limited,Zhuzhou Hunan 412001,China
    3.Tangshan Institute,Southwest Jiaotong University,Tangshan Hebei 063010,China
  • Received:2022-09-15 Revised:2022-12-20 Accepted:2023-01-05 Online:2023-03-02 Published:2023-08-10
  • Contact: Meijia LIANG
  • About author:LIU Xinwu, born in 1989, M. S., assistant engineer. His research interests include big data analysis.
    HU Xiaopeng, born in 1972, M. S., associate professor. His research interests include image processing, intelligent system.
  • Supported by:
    Hebei Provincial Natural Science Foundation(F2022105033)


梁美佳1(), 刘昕武2, 胡晓鹏1,3   

  1. 1.西南交通大学 计算机与人工智能学院,成都 611756
    2.株洲中车时代电气股份有限公司 数据与智能技术中心,湖南 株洲 412001
    3.西南交通大学 唐山研究院,河北 唐山 063010
  • 通讯作者: 梁美佳
  • 作者简介:刘昕武(1989—),男,湖南株洲人,助理工程师,硕士,主要研究方向:大数据分析
  • 基金资助:


Train assisted driving depends on the real-time detection of train operating environment. There are abundant small targets in the images of train operating environment. Compared with large and medium targets, small targets with the proportion of less than 1% of original image have problems of high missed detection and poor detection accuracy due to low resolution. Therefore, a target detection algorithm based on improved YOLOv3 in train operating environment was proposed, namely YOLOv3-TOEI (YOLOv3-Train Operating Environment Image). Firstly, k-means clustering algorithm was used to optimize the anchor to speed up the convergence of the network. Then, dilated convolution was embedded in DarkNet-53 to expand the receptive field, and Dense convolutional Network (DenseNet) was introduced to obtain richer low-level details of the image. Finally, the unidirectional feature fusion structure of original YOLOv3 was improved to bidirectional and adaptive feature fusion structure, which realized the effective combination of deep and shallow features and improved the detection effect of the network on multi-scale targets (especially small targets). Experimental results show that compared with original YOLOv3 algorithm, YOLOv3-TOEI algorithm has the mean Average Precision (mAP)@0.5 reached 84.5%, which increased by 12.2%, and the Frames Per Second (FPS) of 83, verifying that this algorithm has better detection ability of small targets in images of train operating environment.

Key words: train assisted driving, small target detection, dilated convolution, Dense convolutional Network (DenseNet), feature fusion, channel attention mechanism


列车辅助驾驶离不开对列车运行环境的实时检测,而列车运行环境图像存在丰富的小目标。与大中型目标相比,目标占原图比例小于1%的小目标由于分辨率低而存在误检率高、检测精度较差的问题,因此提出一种基于改进YOLOv3的列车运行环境目标检测算法YOLOv3-TOEI (YOLOv3-Train Operating Environment Image)。首先,利用k-means聚类算法优化anchor,从而提高网络的收敛速度;然后,在DarkNet-53中嵌入空洞卷积以增大感受野,并引入稠密卷积网络(DenseNet)获取更丰富的图像底层细节信息;最后,将原始YOLOv3的单向特征融合结构改进为双向自适应特征融合结构,从而实现深浅层特征的有效结合,并提高网络对多尺度目标(特别是小目标)的检测效果。实验结果表明,与原YOLOv3算法相比,YOLOv3-TOEI算法的平均精度均值(mAP)@0.5达到84.5%,提升了12.2%,每秒传输帧数(FPS)为83,拥有更好的列车运行环境图像小目标检测能力。

关键词: 列车辅助驾驶, 小目标检测, 空洞卷积, 稠密卷积网络, 特征融合, 通道注意力机制

CLC Number: