关键词: 盲道场景识别, 迁移学习, 深度神经网络, 移动网络模型, 显著性检测

Abstract: In order to help the person with visual impairments to detect barriers around blind road, a novelty real-time detection for obstacle scene detection of blind road is proposed. The method is based on transfer learning and feature integration with saliency detection by discriminative region. Firstly, the bottleneck descriptors and saliency map descriptors are extracted through discriminative regional feature integration, and then these descriptors are incorporated together. Secondly, the new model is trained again. Finally, the model is used for real time road scene detection with Softmax function. In the experiments, samples of blind road images are collected at different areas in Chengdu for training and validation with difference model cases. And then, the inference models in real time scene with the embedded device are tested. Experimental results show that the proposed method has advantages on both running time and classification precision.

Key words: blind road scene detection, transfer learning, deep neural networks, Mobilenet, saliency detection