Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (3): 883-889.DOI: 10.11772/j.issn.1001-9081.2021030384
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Tianmin DENG, Guotao MAO(), Zhenhao ZHOU, Zhijian DUAN
Received:
2021-03-15
Revised:
2021-06-22
Accepted:
2021-06-23
Online:
2022-04-09
Published:
2022-03-10
Contact:
Guotao MAO
About author:
DENG Tianmin, born in 1979, Ph. D., associate professor. His research interests include traffic big data, traffic environment perception.Supported by:
通讯作者:
冒国韬
作者简介:
邓天民(1979—),男,四川阆中人,副教授,博士,主要研究方向:交通大数据、交通环境感知基金资助:
CLC Number:
Tianmin DENG, Guotao MAO, Zhenhao ZHOU, Zhijian DUAN. Road vehicle detection and recognition algorithm based on densely connected convolutional neural network[J]. Journal of Computer Applications, 2022, 42(3): 883-889.
邓天民, 冒国韬, 周臻浩, 段志坚. 基于密集连接卷积神经网络的道路车辆检测与识别算法[J]. 《计算机应用》唯一官方网站, 2022, 42(3): 883-889.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021030384
网络结构 | mAP/% | 检测速度/(frame·s-1) |
---|---|---|
YOLOv4 | 97.41 | 45.72 |
YOLOv4+密集连接 | 97.89 | 40.35 |
YOLOv4+跨层连接 | 97.35 | 46.41 |
YOLOv4+深度残差 | 96.44 | 55.92 |
DR-YOLOv4特征提取网络 | 96.93 | 51.39 |
Tab. 1 Influence comparison of different feature extraction networks on model performance
网络结构 | mAP/% | 检测速度/(frame·s-1) |
---|---|---|
YOLOv4 | 97.41 | 45.72 |
YOLOv4+密集连接 | 97.89 | 40.35 |
YOLOv4+跨层连接 | 97.35 | 46.41 |
YOLOv4+深度残差 | 96.44 | 55.92 |
DR-YOLOv4特征提取网络 | 96.93 | 51.39 |
网络结构 | mAP/% | 检测速度/(frame·s-1) |
---|---|---|
YOLOv4 | 97.41 | 45.72 |
YOLOv4+跳跃连接 | 97.47 | 45.39 |
YOLOv4+多尺度特征检测 | 97.98 | 39.87 |
DR-YOLOv4特征融合网络 | 98.09 | 39.68 |
Tab. 2 Influence comparison of different feature fusion networks on model performance
网络结构 | mAP/% | 检测速度/(frame·s-1) |
---|---|---|
YOLOv4 | 97.41 | 45.72 |
YOLOv4+跳跃连接 | 97.47 | 45.39 |
YOLOv4+多尺度特征检测 | 97.98 | 39.87 |
DR-YOLOv4特征融合网络 | 98.09 | 39.68 |
13×13 特征图 | 26×26 特征图 | 52×52 特征图 | 104×104 特征图 | mAP/% | 检测速度/ (frame·s-1) |
---|---|---|---|---|---|
3 | 3 | 3 | 3 | 98.09 | 39.68 |
2 | 4 | 3 | 3 | 98.25 | 41.83 |
2 | 3 | 4 | 3 | 98.27 | 42.51 |
4 | 2 | 2 | 4 | 98.01 | 39.14 |
2 | 4 | 4 | 2 | 98.19 | 40.63 |
1 | 3 | 4 | 4 | 98.13 | 39.64 |
4 | 4 | 2 | 2 | 97.96 | 37.92 |
Tab. 3 Influence comparison of different anchor allocation strategies on model performance
13×13 特征图 | 26×26 特征图 | 52×52 特征图 | 104×104 特征图 | mAP/% | 检测速度/ (frame·s-1) |
---|---|---|---|---|---|
3 | 3 | 3 | 3 | 98.09 | 39.68 |
2 | 4 | 3 | 3 | 98.25 | 41.83 |
2 | 3 | 4 | 3 | 98.27 | 42.51 |
4 | 2 | 2 | 4 | 98.01 | 39.14 |
2 | 4 | 4 | 2 | 98.19 | 40.63 |
1 | 3 | 4 | 4 | 98.13 | 39.64 |
4 | 4 | 2 | 2 | 97.96 | 37.92 |
实验序号 | 特征提取 网络改进 | 特征融合 网络改进 | 先验框 聚类与分配 | 各类别AP/% | mAP/% | 检测速度/ (frame·s-1) | ||
---|---|---|---|---|---|---|---|---|
Car | Van | Truck | ||||||
A | ○ | ○ | ○ | 95.68 | 97.72 | 98.83 | 97.41 | 45.72 |
B | ● | ○ | ○ | 95.22 | 97.23 | 98.35 | 96.93 | 51.39 |
C | ○ | ● | ○ | 97.09 | 98.23 | 98.94 | 98.09 | 39.68 |
D | ○ | ○ | ● | 96.60 | 98.37 | 98.92 | 97.96 | 48.54 |
E | ● | ● | ○ | 96.69 | 97.76 | 98.49 | 97.65 | 46.17 |
F | ○ | ● | ● | 98.08 | 98.91 | 99.09 | 98.69 | 41.88 |
G | ● | ○ | ● | 96.17 | 97.89 | 98.45 | 97.50 | 53.92 |
H | ● | ● | ● | 97.48 | 98.51 | 98.65 | 98.21 | 48.05 |
Tab. 4 Analysis of influence of different experimental schemes on model performance
实验序号 | 特征提取 网络改进 | 特征融合 网络改进 | 先验框 聚类与分配 | 各类别AP/% | mAP/% | 检测速度/ (frame·s-1) | ||
---|---|---|---|---|---|---|---|---|
Car | Van | Truck | ||||||
A | ○ | ○ | ○ | 95.68 | 97.72 | 98.83 | 97.41 | 45.72 |
B | ● | ○ | ○ | 95.22 | 97.23 | 98.35 | 96.93 | 51.39 |
C | ○ | ● | ○ | 97.09 | 98.23 | 98.94 | 98.09 | 39.68 |
D | ○ | ○ | ● | 96.60 | 98.37 | 98.92 | 97.96 | 48.54 |
E | ● | ● | ○ | 96.69 | 97.76 | 98.49 | 97.65 | 46.17 |
F | ○ | ● | ● | 98.08 | 98.91 | 99.09 | 98.69 | 41.88 |
G | ● | ○ | ● | 96.17 | 97.89 | 98.45 | 97.50 | 53.92 |
H | ● | ● | ● | 97.48 | 98.51 | 98.65 | 98.21 | 48.05 |
模型 | 各类别AP/% | mAP/% | 检测速度/(frames·s-1) | ||
---|---|---|---|---|---|
Car | Van | Truck | |||
Faster R-CNN | 78.25 | 72.93 | 80.09 | 77.09 | 12.47 |
SSD300 | 81.59 | 74.82 | 87.36 | 81.26 | 56.71 |
SSD512 | 84.81 | 70.72 | 82.98 | 79.50 | 26.24 |
YOLOv3 | 90.53 | 92.39 | 94.87 | 92.60 | 47.91 |
YOLOv4 | 95.68 | 97.72 | 98.83 | 97.41 | 45.72 |
DR-YOLOv4 | 97.48 | 98.51 | 98.65 | 98.21 | 48.05 |
Tab. 5 Comparison of effects of different target detection models on KITTI dataset
模型 | 各类别AP/% | mAP/% | 检测速度/(frames·s-1) | ||
---|---|---|---|---|---|
Car | Van | Truck | |||
Faster R-CNN | 78.25 | 72.93 | 80.09 | 77.09 | 12.47 |
SSD300 | 81.59 | 74.82 | 87.36 | 81.26 | 56.71 |
SSD512 | 84.81 | 70.72 | 82.98 | 79.50 | 26.24 |
YOLOv3 | 90.53 | 92.39 | 94.87 | 92.60 | 47.91 |
YOLOv4 | 95.68 | 97.72 | 98.83 | 97.41 | 45.72 |
DR-YOLOv4 | 97.48 | 98.51 | 98.65 | 98.21 | 48.05 |
模型 | 各类别AP/% | mAP/% | 检测速度/ (frames·s-1) | ||
---|---|---|---|---|---|
Car | Bus | Truck | |||
YOLOv4 | 61.35 | 56.92 | 53.71 | 57.32 | 41.26 |
DR-YOLOv4 | 62.23 | 58.07 | 54.42 | 58.24 | 43.50 |
Tab. 6 Test results on BDD100K dataset before and after model improvement
模型 | 各类别AP/% | mAP/% | 检测速度/ (frames·s-1) | ||
---|---|---|---|---|---|
Car | Bus | Truck | |||
YOLOv4 | 61.35 | 56.92 | 53.71 | 57.32 | 41.26 |
DR-YOLOv4 | 62.23 | 58.07 | 54.42 | 58.24 | 43.50 |
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