中圖分類(lèi)號(hào)： TN13 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.191412 中文引用格式： 李太文，范昕煒. 基于Faster R-CNN的道路裂縫識(shí)別[J].電子技術(shù)應(yīng)用，2020，46(7)：53-56，59. 英文引用格式： Li Taiwen，F(xiàn)an Xinwei. Road crevice recognition based on Faster R-CNN[J]. Application of Electronic Technique，2020，46(7)：53-56，59.
Road crevice recognition based on Faster R-CNN
Li Taiwen，F(xiàn)an Xinwei
School of Quality and Safety Engineering，China Jiliang University，Hangzhou 310000，China
Abstract： Traditional road crack recognition methods are based on R-CNN, SPPnet, HOG+SVM and other methods, but the recognition accuracy is low and the detection speed is slow. In view of these shortcomings, a road crack recognition method based on Faster R-CNN is proposed. Firstly, road crack images were collected to build Pascal VOC data set. Secondly, the TensorFlow deep learning framework developed based on Google trains the Faster R-CNN with data sets and analyzes various performance parameters. The experimental results show that the training loss can be reduced to 0.188 5 and the AP value can reach 0.780 2 in the case of 20 000 iterations, achieving good results.
Key words : machine learning；deep learning；CNN；road cracks；Faster-RCNN
