标题:Deep Saliency With Channel-Wise Hierarchical Feature Responses for Traffic Sign Detection
作者:Li C.; Chen Z.; Wu Q.M.J.; Liu C.
作者机构:[Li, C] School of Control Science and Engineering, Shandong University, Jinan 250061, China.;[ Chen, Z] School of Control Science and Engineering, Sha 更多
来源:IEEE Transactions on Intelligent Transportation Systems
出版年:2018
DOI:10.1109/TITS.2018.2867183
关键词:channel-wise feature responses; Convolutional neural networks; Deep saliency; Feature extraction; hierarchical feature refinement; Image color analysis; Kernel; Saliency detection; Shape; squeeze-and-excitation-residual network; traffic sign detection.; Visualization
摘要:Traffic sign detection is challenging in cases of a complex background, occlusions, distortions, and so on. To overcome the above-mentioned challenges, this paper pays close attention to channel-wise feature responses to propose an end-to-end deep learning-based saliency traffic sign detection method. Our model contains three main components: channel-wise coarse feature extraction (CCFE), channel-wise hierarchical feature refinement (CHFR), and hierarchical feature map fusion (HFMF). In addition, it is based on the squeeze-and-excitation-residual network to explicitly model the inter dependences between the channels of its convolution features at a slight computational cost. We first apply CCFE to produce coarse feature maps with much information loss. To make full use of spatial information and fine details, CHFR is executed to refine hierarchical features. After that, HFMF is used to fuse hierarchical feature maps to generate the final traffic sign saliency map. Compared with other five traffic sign detection methods, the experimental results demonstrate the efficiency (a real-time speed) and superior performance of the proposed method according to comprehensive evaluations over three benchmark data sets. IEEE
收录类别:SCOPUS
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054395949&doi=10.1109%2fTITS.2018.2867183&partnerID=40&md5=6b5f932726607e7d8c24c12924ea331e
TOP