Abstract
Due to the pervasive employment of trams, the measurements to keep the transit of trams safe are necessary and emergent. In this sense, we put forward a Neural Network based on Convolutional Neural Network, in which we made some modifications to make it more flexible. Such as passthrough layer to ensure some small objects detectable, anchor boxes to ensure a high-speed detection, and batch-normalization layers to make the network be malleable for objects with different distributions. With this network, we can efficiently detect possible obstacles, such as pedestrians, cars and some other objections that may endanger the trams. We test the network among several databases with 5000 samples, and the average accuracy rate is 94.12%, the average detecting speed is 30 FPS, the smallest detectable object’s size is 20 × 20 pixels, these all show remarkable result.
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Acknowledgements
This work was supported by the National Key Research and Development Plan (No. 2018YFB1201601-07).
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Li, Y., Cai, G. (2020). Deep-Learning-Based Detection of Obstacles in Transit on Trams. In: Wang, W., Baumann, M., Jiang, X. (eds) Green, Smart and Connected Transportation Systems. Lecture Notes in Electrical Engineering, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-15-0644-4_82
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DOI: https://doi.org/10.1007/978-981-15-0644-4_82
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