Pedestrian detection based on improved LeNet-5 convolutional neural network
| Autor: | Chuan-Wei Zhang, Meng-Yue Yang, Hong-Jun Zeng, Jian-Ping Wen |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Journal of Algorithms & Computational Technology, Vol 13 (2019) |
| Druh dokumentu: | article |
| ISSN: | 1748-3026 17483026 |
| DOI: | 10.1177/1748302619873601 |
| Popis: | In this article, according to the real-time and accuracy requirements of advanced vehicle-assisted driving in pedestrian detection, an improved LeNet-5 convolutional neural network is proposed. Firstly, the structure of LeNet-5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA-Fast R-CNN and classical LeNet-5 CNN algorithm. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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