Abstract
Using low-cost visual sensors to assist indoor and outdoor navigation is an important method to solve the problem of visually impaired people living and going out. To this end, we proposed an obstacle-detection method for visually impaired people based on semantic segmentation. We use the semantic segmentation method to determine which targets in the camera view field need to be noticed and use the related information to establish a real-time local map. At the same time, we propose a method to fuse semantic information with local point clouds, achieving obstacle detection based on probability fusion. Finally, the distance between the interested target and the camera will be returned and sent to the user. The proposed method can achieve visual navigation with more than ten frames per second (fps), lower than 0.3 m detection accuracy, and smaller than 4 MB generated model, which is also compatible with multiple cameras and control terminals.
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References
Bourne, R.R.A., Steinmetz, J.D., Flaxman, S., et al.: Trends in prevalence of blindness and distance and near vision impairment over 30 years: an analysis for the global burden of disease study. Lancet Glob. Health 9(2), e130–e143 (2021)
Juneja, S., Joshi, P.: Design and development of a low cost and reliable writing aid for visually impaired based on Morse code communication. Technol. Disabil. 32(2), 59–67 (2020)
Isaksson, J., Jansson, T., Nilsson, J.: Desire of use: a hierarchical decomposition of activities and its application on mobility of by blind and low-vision individuals. IEEE Trans. Neural Syst. Rehabil. Eng. 28(5), 1146–1156 (2020)
Xiong, Z., Huang, X.: Comparison of the static and dynamic vibrotactile interactive perception of walking navigation assistants for limited vision people. iEEE Access 10, 42261–42267 (2022)
Joseph, A.M., Kian, A., Begg, R.: State-of-the-art review on wearable obstacle detection systems developed for assistive technologies and footwear. Sensors 2023(23), 2802 (2023)
Adarsh, S., Kaleemuddin, S.M., Bose, D., Ramachandran, K.I.: Performance comparison of infrared and ultrasonic sensors for obstacles of different materials in vehicle/ robot navigation applications. IOP Conf. Ser. Mater. Sci. Eng. 149(1), 012141 (2016)
Marti, E.D., de Miguel, M.A., Garcia, F., Perez, J.: A Review of sensor technologies for perception in automated driving. IEEE Intell. Transp. Syst. Mag. 11(4), 94–108 (2019)
Fang, Z., Zhao, S., Wen, S., Zhang, Y.: A real-time 3d perception and reconstruction system based on a 2d laser scanner. J. Sensors 2018, 2937694 (2018)
Yu, H., Zhu, J., Wang, Y., Jia, W., Sun, M., Tang, Y.: Obstacle classification and 3D measurement in unstructured environments based on ToF cameras. Sensors 2014(14), 10753–10782 (2014)
Discant, A., Rogozan, A., Rusu, C., Bensrhair, A.: Sensors for obstacle detection—a survey. In: Proceedings of the 2007 30th International Spring Seminar on Electronics Technology (ISSE), Cluj-Napoca, Romania (2007)
Jégou, S., Drozdzal, M., Vazquez, D,, Romero, A., Bengio, Y.: The one hundred layers tiramisu: fully convolutional densenets for semantic segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1175–1183. Honolulu, HI, USA (2017)
Jain, S.D., Xiong, B., Grauman, K.: FusionSeg: learning to combine motion and appearance for fully automatic segmentation of generic objects in videos. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2117–2126. IEEE, Honolulu, USA (2017)
He, K., Zhang, X., Ren, S., & Sun, J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Las Vegas, USA, pp. 770–778 (2016)
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: Proceedings of the International Conference on Intelligent Robot Systems (IROS) (2012)
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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Chen, Z., Liu, X., Liu, D., Tang, X., Huang, Q., Arai, T. (2024). An Obstacle Detection Method for Visually Impaired People Based on Semantic Segmentation. In: Sun, F., Li, J. (eds) Cognitive Computation and Systems. ICCCS 2023. Communications in Computer and Information Science, vol 2029. Springer, Singapore. https://doi.org/10.1007/978-981-97-0885-7_3
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DOI: https://doi.org/10.1007/978-981-97-0885-7_3
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