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Home > Archive>Volume 40, Issue 7, 2017 >32-36. DOI:10.11835/j.issn.1000-582X.2017.07.005
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Vehicle detection based on faster-RCNN
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    Abstract:

    As one of the object detection, vehicle detection, which has been a hot research area in recent years, is one of the important application in intelligent transportation system. To figure out the problem that vehicle detection is lack of the ability of vehicle category recognition, we adopted the strategy of integrating the Faster-RCNN(region-based convolutional neural networks)model with 3 different convolutional neural networks (ZF, VGG-16 and ResNet-101)respectively. By comparing the vehicle category recognition results of the 3 integrating strategies on BIT-Vehicle database and CompCars database, the strategy integrating the Faster-RCNN model with ResNet-101 shows the best result among the 3 models and recognition accuracy reaches 91.3% on BIT-Vehicle database. On the migration test CompCars database, 3 strategy models show good generalization ability.

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桑军,郭沛,项志立,罗红玲,陈欣. Faster-RCNN的车型识别分析[J].重庆大学学报,2017,40(7):32~36

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  • Received:February 10,2017
  • Online: August 01,2017
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