| 摘要: |
| 我国机动车保有量急速增长，产生一系列严重的安全与交通问题.与此同时，视频图像文件呈爆炸式增长，为公安的监控、刑侦以及案件的侦破带来了很大的困扰.车辆目标检测与识别越来越受到人们的关注，研究一种高效而准确的车辆目标检测方法意义重大.在YOLO目标检测框架的基础上，设计了一种卷积神经网络的车辆检测及其车型粗粒度识别方法.网络结构采用多层感知机卷积层，增加特征映射的非线性处理能力；移除原来模型中的全连接层，利用锚点框预测目标的边界框，在降低模型复杂度的同时提高了目标检测的召回率.实验结果表明，与主流的目标检测方法相比，该车辆目标检测方法在处理速度和准确度上都有提高，在迭代20 000次的情况下，平均准确率为94.7%. |
| 关键词: 车辆检测 卷积神经网络 多层感知机 粗粒度 车辆识别 |
| DOI： |
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| 基金项目:国家自然科学基金（61379048，61672508）；河北省重点研发计划（17395602D） |
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| Convolutional Neural Network Applied on Fast Vehicle Objects Detection |
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CHEN Hong-Cai1,2, CHENG Yu1,2, ZHANG Chang-You3
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1.Hebei Academy of Sciences Institute of Applied Mathematics, Shijiazhuang 050081, China;2.Hebei Authentication Technology Engineering Research Center, Shijiazhuang 050081, China;3.State Key Laboratory of Computing Science, Institute of Software, The Chinese Academy of Sciences, Beijing 100190, China
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| Abstract: |
| With the rapid growth of the number of motor vehicles in China, inevitably there would appear a series of severe problems concerning safety and traffics. At the same time, the video image files are increasing at an explosive speed, which has brought a lot of trouble to the public security monitoring, criminal investigation and the case detection. It is important to research an efficient and accurate vehicle detection algorithm. This paper proposes a new deep convolution neural networks frame for vehicle detection and coarse grained recognition based YOLO method. Multilayer perceptron convolution layers are added in the new network structure framework to enhance nonlinear ability of feature mapping. This framework deletes fully connected layers and predicts the bounding boxes using anchor boxes. The new framework improves recall rates of object detection and effectively reduces computational complexity. Experimental results show that the improved method has an average accuracy of 94.7% for vehicle detection under iteration 20000 times. Compared with other detection methods, the processing speed and accuracy of the new method have been improved. |
| Key words: vehicle detection convolution neural networks multilayer perceptrons coarse-grained vehicle recognition |
