《计算机应用》唯一官方网站 ›› 2023, Vol. 43 ›› Issue (8): 2611-2618.DOI: 10.11772/j.issn.1001-9081.2022091343
• 多媒体计算与计算机仿真 • 上一篇
收稿日期:
2022-09-15
修回日期:
2022-12-20
接受日期:
2023-01-05
发布日期:
2023-03-02
出版日期:
2023-08-10
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:
Meijia LIANG1(), Xinwu LIU2, Xiaopeng HU1,3
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.Supported by:
摘要:
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。
中图分类号:
梁美佳, 刘昕武, 胡晓鹏. 基于改进YOLOv3的列车运行环境图像小目标检测算法[J]. 计算机应用, 2023, 43(8): 2611-2618.
Meijia LIANG, Xinwu LIU, Xiaopeng HU. Small target detection algorithm for train operating environment image based on improved YOLOv3[J]. Journal of Computer Applications, 2023, 43(8): 2611-2618.
输入特征层 | 操作 | 上/下采样倍数 | 输出特征图 |
---|---|---|---|
256×64×64 | 下采样 | 4 | 256×16×16 |
512×32×32 | 下采样 | 2 | 512×16×16 |
1 024×16×16 | — | — | 1 024×16×16 |
表1 不同输入特征层的具体上/下采样操作
Tab. 1 Specific up-sampling or down-sampling operations for different input feature layers
输入特征层 | 操作 | 上/下采样倍数 | 输出特征图 |
---|---|---|---|
256×64×64 | 下采样 | 4 | 256×16×16 |
512×32×32 | 下采样 | 2 | 512×16×16 |
1 024×16×16 | — | — | 1 024×16×16 |
类别 | 训练集样本数 | 测试集样本数 |
---|---|---|
共计 | 26 164 | 6 491 |
person | 5 861 | 1 413 |
locomotive | 8 141 | 2 141 |
car | 284 | 89 |
signal_light | 5 758 | 1 481 |
truck | 195 | 39 |
sign | 1 138 | 275 |
obstacle | 4 787 | 1 053 |
表2 训练集和测试集信息
Tab. 2 Information of training set and test set
类别 | 训练集样本数 | 测试集样本数 |
---|---|---|
共计 | 26 164 | 6 491 |
person | 5 861 | 1 413 |
locomotive | 8 141 | 2 141 |
car | 284 | 89 |
signal_light | 5 758 | 1 481 |
truck | 195 | 39 |
sign | 1 138 | 275 |
obstacle | 4 787 | 1 053 |
空洞卷积 | 稠密模块 | 特征融合 | k-means | mAP@0.5 | F1 | FPS |
---|---|---|---|---|---|---|
— | — | — | — | 0.753 | 0.777 | 145 |
— | — | — | √ | 0.802 | 0.823 | 145 |
√ | — | — | — | 0.763 | 0.786 | 136 |
√ | √ | — | — | 0.780 | 0.796 | 131 |
— | — | √ | — | 0.779 | 0.798 | 85 |
√ | √ | √ | — | 0.792 | 0.808 | 83 |
√ | √ | √ | √ | 0.845 | 0.861 | 83 |
表3 模块消融实验结果对比
Tab. 3 Comparison of experimental results of module ablation
空洞卷积 | 稠密模块 | 特征融合 | k-means | mAP@0.5 | F1 | FPS |
---|---|---|---|---|---|---|
— | — | — | — | 0.753 | 0.777 | 145 |
— | — | — | √ | 0.802 | 0.823 | 145 |
√ | — | — | — | 0.763 | 0.786 | 136 |
√ | √ | — | — | 0.780 | 0.796 | 131 |
— | — | √ | — | 0.779 | 0.798 | 85 |
√ | √ | √ | — | 0.792 | 0.808 | 83 |
√ | √ | √ | √ | 0.845 | 0.861 | 83 |
类别 | YOLOv3 | YOLOv3-TOEI | 类别 | YOLOv3 | YOLOv3-TOEI |
---|---|---|---|---|---|
person | 0.838 | 0.880 | truck | 0.928 | 0.951 |
locomotive | 0.957 | 0.963 | sign | 0.570 | 0.779 |
car | 0.880 | 0.913 | obstacle | 0.528 | 0.680 |
signal_light | 0.571 | 0.745 |
表4 YOLOv3和YOLOv3-TOEI的各类精度比较
Tab. 4 Comparison of each category AP between YOLOv3 and YOLOv3-TOEI
类别 | YOLOv3 | YOLOv3-TOEI | 类别 | YOLOv3 | YOLOv3-TOEI |
---|---|---|---|---|---|
person | 0.838 | 0.880 | truck | 0.928 | 0.951 |
locomotive | 0.957 | 0.963 | sign | 0.570 | 0.779 |
car | 0.880 | 0.913 | obstacle | 0.528 | 0.680 |
signal_light | 0.571 | 0.745 |
算法 | mAP@0.5 | F1 | FPS |
---|---|---|---|
YOLOv3 | 0.753 | 0.777 | 145 |
YOLOv3-spp | 0.748 | 0.772 | 145 |
YOLOv3-tiny | 0.602 | 0.632 | 431 |
YOLOv4 | 0.775 | 0.710 | 64 |
Faster RCNN | 0.856 | 0.876 | 21 |
SSD | 0.719 | 0.752 | 109 |
YOLOv3-TOEI | 0.845 | 0.861 | 83 |
表5 YOLOv3-TOEI与其他算法的综合比较
Tab. 5 Comprehensive comparison of YOLOv3-TOEI and other algorithms
算法 | mAP@0.5 | F1 | FPS |
---|---|---|---|
YOLOv3 | 0.753 | 0.777 | 145 |
YOLOv3-spp | 0.748 | 0.772 | 145 |
YOLOv3-tiny | 0.602 | 0.632 | 431 |
YOLOv4 | 0.775 | 0.710 | 64 |
Faster RCNN | 0.856 | 0.876 | 21 |
SSD | 0.719 | 0.752 | 109 |
YOLOv3-TOEI | 0.845 | 0.861 | 83 |
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