Deep Neural Networks Approach for Monitoring Vehicles on the Highway
DOI:
10.33395/sinkron.v4i2.10553Keywords:
Deep Learning, Deep Neural Network, Vehicle Monitoring, YOLOv3, MobileNet-SSDAbstract
Vehicle classification and detection aims to extract certain types of vehicle information from images or videos containing vehicles and is one of the important things in a smart transportation system. However, due to the different size of the vehicle, it became a challenge that directly and interested many researchers . In this paper, we compare YOLOv3's one-stage detection method with MobileNet-SSD for direct vehicle detection on a highway vehicle video dataset specifically recorded using two cellular devices on highway activities in Medan City, producing 42 videos, both methods evaluated based on Mean Average Precision (mAP) where YOLOv3 produces better accuracy of 81.9% compared to MobileNet-SSD at 67.9%, but the size of the resulting video file detection is greater. Mobilenet-SSD performs faster with smaller video output sizes, but it is difficult to detect small objects.
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References
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Chen, L., Ye, F., Ruan, Y., Fan, H., & Chen, Q. (2018). An algorithm for highway vehicle detection based on convolutional neural network. Eurasip Journal on Image and Video Processing, 2018(1), 1–7. https://doi.org/10.1186/s13640-018-0350-2
Fedorov, A., Nikolskaia, K., Ivanov, S., Shepelev, V., & Minbaleev, A. (2019). Traffic flow estimation with data from a video surveillance camera. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0234-z
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., … Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Retrieved from http://arxiv.org/abs/1704.04861
Kevin, K., Gunawan, N., Zagoto, M. E. K., Laurentius, L., & Husein, A. M. (2019). Comparison Of Cellular Video Quality For Object Detection Using Neural Network Convolution. SinkrOn, 4(1), 260. https://doi.org/10.33395/sinkron.v4i1.10248
Lestari, P., Manik, D. H. D., Br Sihotang, N. L., & Husein, A. M. (2019). Video Surveillance System with a Deep Learning Approach. SinkrOn, 4(1), 263. https://doi.org/10.33395/sinkron.v4i1.10247
Li, X., Liu, Y., Zhao, Z., Zhang, Y., & He, L. (2018). A Deep Learning Approach of Vehicle Multitarget Detection from Traffic Video. Journal of Advanced Transportation, 2018, 1–11. https://doi.org/10.1155/2018/7075814
Li, Yinghua, Song, B., Kang, X., Du, X., & Guizani, M. (2018). Vehicle-type detection based on compressed sensing and deep learning in vehicular networks. Sensors (Switzerland), 18(12), 1–15. https://doi.org/10.3390/s18124500
Li, Yiting, Huang, H., Xie, Q., Yao, L., & Chen, Q. (2018). Research on a surface defect detection algorithm based on MobileNet-SSD. Applied Sciences (Switzerland), 8(9). https://doi.org/10.3390/app8091678
Liu, K., & Wang, J. (2019). Fast dynamic vehicle detection in road scenarios based on pose estimation with convex-hull model. Sensors (Switzerland), 19(14). https://doi.org/10.3390/s19143136
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9905 LNCS, 21–37. https://doi.org/10.1007/978-3-319-46448-0_2
Nguyen, H. (2019). Improving Faster R-CNN Framework for Fast Vehicle Detection. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/3808064
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 779–788. https://doi.org/10.1109/CVPR.2016.91
Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-Janua, 6517–6525. https://doi.org/10.1109/CVPR.2017.690
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. Retrieved from http://arxiv.org/abs/1804.02767
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 4510–4520. https://doi.org/10.1109/CVPR.2018.00474
Sang, J., Wu, Z., Guo, P., Hu, H., Xiang, H., Zhang, Q., & Cai, B. (2018). An improved YOLOv2 for vehicle detection. Sensors (Switzerland), 18(12). https://doi.org/10.3390/s18124272
Song, H., Liang, H., Li, H., Dai, Z., & Yun, X. (2019). Vision-based vehicle detection and counting system using deep learning in highway scenes. European Transport Research Review, Vol. 11. https://doi.org/10.1186/s12544-019-0390-4
Wang, H., & Cai, Y. (2014). A Multistep Framework for Vision Based Vehicle Detection. Journal of Applied Mathematics, 2014(2). https://doi.org/10.1155/2014/876451
Wang, X., Hua, X., Xiao, F., Li, Y., Hu, X., & Sun, P. (2018). Multi-object detection in traffic scenes based on improved SSD. Electronics (Switzerland), 7(11). https://doi.org/10.3390/electronics7110302
Yang, C., Li, W., & Lin, Z. (2018). Vehicle Object Detection in Remote Sensing Imagery Based on Multi-Perspective Convolutional Neural Network. ISPRS International Journal of Geo-Information, 7(7), 249. https://doi.org/10.3390/ijgi7070249
Ye, T., Wang, B., Song, P., & Li, J. (2018). Automatic railway traffic object detection system using feature fusion refine neural network under shunting mode. Sensors (Switzerland), 18(6). https://doi.org/10.3390/s18061916
Zhang, F., Li, C., & Yang, F. (2019). Vehicle detection in urban traffic surveillance images based on convolutional neural networks with feature concatenation. Sensors (Switzerland), Vol. 19. https://doi.org/10.3390/s19030594
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Husein, A. M., Christopher, C., Gracia, A., Brandlee, R., & Hasibuan, M. H. (2020). Deep Neural Networks Approach for Monitoring Vehicles on the Highway. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 4(2), 163-171. https://doi.org/10.33395/sinkron.v4i2.10553
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