Acta Optica Sinica, Volume. 39, Issue 6, 0610004(2019)
Segmentation and Recognition Algorithm for High-Speed Railway Scene
Fig. 1. Railway scene and track area
Fig. 2. Edge feature map of railway scene
Fig. 3. Distribution of linear character after Hough transformation
Fig. 4. Gaussian convolution kernels rotated by adaptive angles. (a) θ=22°; (b) θ=38°; (c) θ=90°; (d) θ=178°
Fig. 5. Procedures of combining fragmented regions. (a) Strong and weak boundaries; (b) distribution of boundary weight; (c) boundaries after deletion of weak points; (d) fragmented regions; (e) distribution of fragmented region area; (f) local areas after combination; (g)-(o) local areas after segmentation
Fig. 6. Schematic of convolutional neural network structure
Fig. 7. Pre-train convolutional kernels using autoencoder network. (a) Structure of autoencoder networks; (b) pre-trained convolution kernels
Fig. 8. Structural schematic of high-speed railway intrusion detecting system
Fig. 9. Comparison diagrams of results of different algorithms for track area recognition. (a) Railway scenes; (b) manually labeled regions; (c) results of MCG algorithm; (d) results of FCN algorithm; (e) results of proposed algorithm
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Yang Wang, Liqiang Zhu, Zujun Yu, Baoqing Guo. Segmentation and Recognition Algorithm for High-Speed Railway Scene[J]. Acta Optica Sinica, 2019, 39(6): 0610004
Category: Image Processing
Received: Feb. 22, 2019
Accepted: Apr. 8, 2019
Published Online: Jun. 17, 2019
The Author Email: Zhu Liqiang (lqzhu@bjtu.edu.cn)