Car Detector Based on YOLOv5 for Parking Management
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36886-8_9
Reference25 articles.
1. Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., Zuair, M.: Deep learning approach for car detection in uav imagery. Remote Sensing 9, 1–15 (2017). https://doi.org/10.3390/rs9040312
2. Chen, H.C., Huang, C.J., Lu, K.H.: Design of a non-processor obu device for parking system based on infrared communication. In: 2017 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW), pp. 297–298 (2017). https://doi.org/10.1109/ICCE-China.2017.7991113
3. Chen, S., Zhang, S., Shang, J., Chen, B., Zheng, N.: Brain-inspired cognitive model with attention for self-driving cars. IEEE Trans. Cognitive Developm. Syst. 11(1), 13–25 (2019). https://doi.org/10.1109/TCDS.2017.2717451
4. Cui, C., et al.: Pp-lcnet: A lightweight CPU convolutional neural network. CoRR abs/ arXiv: 2109.15099 (2021)
5. Ding, X., Yang, R.: Vehicle and parking space detection based on improved yolo network model. J. Phys: Conf. Ser. 1325, 012084 (2019). https://doi.org/10.1088/1742-6596/1325/1/012084
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