基于改进Faster R-CNN的车辆乘员数量检测方法

金鑫; 胡英

基于改进Faster R-CNN的车辆乘员数量检测方法

金鑫,
胡英

大连海事大学船舶电气工程学院，辽宁大连 116026

基金项目:

国家自然科学基金 61973049

详细信息

作者简介:
金鑫（1996），男，硕士研究生，主要研究方向为计算机视觉、深度学习、目标检测。E-mail: jin_xin@dlmu.edu.cn

中图分类号: TP391
计量
- 文章访问数: 200
- HTML全文浏览量: 110
- PDF下载量: 29
- 被引次数: 0
出版历程
- 收稿日期: 2020-02-19
- 修回日期: 2020-09-02
- 刊出日期: 2020-11-20

Detection of Vehicle Crews Based on Modified Faster R-CNN

JIN Xin,
HU Ying

Dalian Maritime University, College of Marine Electrical Engineering, Dalian 116026, China

摘要

摘要: 针对现有以雷达技术和红外热成像技术为代表的HOV（High occupancy vehiclelane）车道车辆乘员数量检测方法可靠性差、准确率低等问题，提出一种基于多光谱红外图像与改进Faster R-CNN（Region-Convolutional Neural Networks）的车辆乘员数量检测方法。通过多光谱红外成像系统获得汽车内部空间图像，结合Faster R-CNN深度学习算法实现乘员数量检测，通过采用全卷积网络结构、多尺度特征预测、使用ROI-Align代替ROI-Pooling等方式增强网络的泛化能力。通过对样据进行K-means聚类得到目标框长宽几何比例先验分布，提高区域生成（region proposal network，RPN）网络训练速度和位置回归准确性。测试结果表明，获得的汽车内部空间图像较为清晰，算法可以实现对乘员数量的检测。经过改进，网络的泛化能力得到增强，单乘员检测的准确率达到88.6%，相比于改进前提高了13.8%，能够满足行业规定大于80%的要求。
- 多光谱红外图像 /
- Faster-RCNN /
- 全卷积 /
- K-means聚类 /
- ROI-Align
Abstract: Existing methods for detecting the number of vehicle occupants in a high-occupancy vehicle (HOV) lane, using radar and infrared thermal imaging technology, exhibit low reliability and low accuracy. To address these limitations, a method for detecting the number of vehicle occupants based on multispectral infrared imaging and an improved Faster regions with convolutional neural networks (R-CNN) algorithm is proposed. The vehicle interior space image is obtained using a multispectral infrared imaging system, and the number of passengers is detected by a Faster R-CNN deep learning algorithm. The generalization ability of the network is enhanced using the full convolution network structure and multiscale feature prediction, and ROI-Align is used instead of ROI-Pooling. Through K-means clustering, the prior distribution of the geometric proportion of the length and width of the target frame is obtained, which improves the training speed and the accuracy of position regression of the region proposal network (RPN). The test results showed that the interior space image was clear, and the algorithm could detect the number of passengers. After its improvement, the generalization ability of the network was enhanced, and the accuracy of single occupant detection reached 88.6%, which was 13.8% higher than before its improvement. This meets the requirements of more than 80% of industry regulations.
- multispectral infrared image /
- faster-RCNN /
- full convolution /
- K-means clustering /
- ROI-Align

HTML全文

图 1 多光谱红外成像效果

Figure 1. Multispectral visual imaging effect

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图 2 Faster R-CNN网络结构图

Figure 2. Faster R-CNN network structure diagram

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图 3 基于VGG-16的特征提取网络

Figure 3. Feature extraction network based on VGG-16

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图 4 RPN网络结构

Figure 4. RPN network structure diagram

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图 5 聚类结果

Figure 5. Clustering results

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图 6 ROI-Pooling和ROI Align过程图

Figure 6. ROI-Pooling and ROI Alignment process diagram

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图 7 RPN loss训练曲线对比

Figure 7. Comparison of RPN loss training curves

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图 8 改进前后检测效果对比

Figure 8. Comparison of detection effect before and after improvement

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表 1 RPN改进前后对比

Table 1. RPN Comparison before and after improvement

Method	Regions	AP/%	Times/ms
Ori_RPN	2000	69.00	235
Our_RPN1	2000	74.43	240
Our_RPN2	2000	75.52	241
Our_RPN1+Net-Improved Our_RPN2+Net-Improved	2000 2000	74.91 76.17	237 237

下载: 导出CSV

表 2 不同算法检测效果对比

Table 2. Comparison of detection results of different algorithms

Network	Accuracy/%					Fps
Network	1	2	3	4	5	Fps
Faster R-cnn	74.8	72.6	69.8	65.4	58.8	17
Ours	88.6	86.2	83.0	78.8	73.2	15
YOLOv3	68.6	65.2	61.8	57.4	51.2	30
Mask R-CNN	82.4	78.8	76.2	72.4	66.8	5

下载: 导出CSV

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