本文選題：塞釘 + 軌道電路��； 參考：《中國鐵道科學》2017年03期

【摘要】：根據高鐵巡檢車所采集軌腰圖像中鐵道塞釘圖像的特點,在既有計算機視覺的目標檢測算法的基礎上,提出基于深度學習的鐵道塞釘自動檢測算法。在目標檢測的區(qū)域選擇階段,借鑒顯著性檢測的思路,提出余譜區(qū)域候選(Spectrum Residual Region Proposal,SRP)算法,即利用含塞釘的軌腰圖像與不含塞釘的軌腰平均圖像之間的頻譜差異,通過快速傅里葉變換,得到兩圖像間的幅度譜差的絕對值(余譜),再通過快速傅里葉反變換及后處理,得到候選目標區(qū)域;然后在目標檢測的特征提取階段,設計塞釘卷積神經網絡(plug Convolution Neural Network,pCNN),該網絡通過4個卷積層、3個池化層、3個非線性變換層、3個規(guī)范化層、2個全連接層和1個泄露層,自動從候選目標區(qū)域逐層提取最能表現塞釘特征的特征圖像;最后基于特征圖像采用支持向量機(SVM)的分類器判斷候選目標區(qū)域是否含有塞釘,從而實現塞釘的自動定位。大量實際測試以及與其他算法比較的結果表明,該算法的檢測效果最優(yōu)。
[Abstract]:According to the characteristics of railway stud images collected by high-speed railway inspection vehicle, an automatic detection algorithm based on depth learning is proposed on the basis of the existing target detection algorithms of computer vision. In the region selection stage of target detection, using the idea of significant detection for reference, this paper proposes a candidate Spectrum Residual Region Proposal Residual Region algorithm for cospectral region, that is, using the spectral difference between the rail waist image with studs and the average rail waist image without studs. The absolute value of amplitude spectral difference between two images (cospectrum) is obtained by fast Fourier transform (FFT), and then the candidate target region is obtained by FFT and post-processing, and then in the feature extraction stage of target detection, Plug Convolution Neural network pCNNs are designed. The network consists of four convolution layers, three pool layers, three nonlinear transformation layers, three normalized layers, two fully connected layers and one leak layer. Finally, the feature image is extracted from the candidate target area layer by layer. Finally, the support vector machine (SVM) classifier is used to determine whether the candidate target region contains studs or not, so as to realize the automatic location of studs. A large number of practical tests and comparison with other algorithms show that the algorithm has the best detection effect.
【作者單位】： 中國鐵道科學研究院基礎設施檢測研究所;
【基金】：國家“九七三”計劃項目(2013CB329400) 中國鐵路總公司科技研究開發(fā)計劃重大項目(2015T003-A) 中國鐵道科學研究院行業(yè)服務技術創(chuàng)新項目(2014YJ052)
【分類號】：TP18;U216.3
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本文編號：1887332