A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS

Authors

  • Agus Mulyanto Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Wisnu Jatmiko Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Petrus Mursanto Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia
  • Purwono Prasetyawan Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia
  • Rohmat Indra Borman Faculty of Engineering and Computer Science, Universitas Teknokrat Indonesia, Bandar Lampung, Indonesia

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2021.14.3.6

Abstract

Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.

References

Litman, T., Autonomous Vehicle Implementation Predictions: Implications for Transport Planning, Victoria Transport Policy Institute, 2018.

Bojarski, M., Del-Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J. & Zhang, X., End to End Learning for Self-driving Cars, arXiv preprint, arXiv:1604.07316, Apr. 2016.

Häne, C., Sattler, T. & Pollefeys, M., Obstacle Detection for Self-driving Cars Using Only Monocular Cameras and Wheel Odometry, International Conference on Intelligent Robots and Systems (IROS), pp. 5101-5108, 2015.

Ramos, S., Gehrig, S., Pinggera, P., Franke, U. & Rother, C., Detecting Unexpected Obstacles for Self-Driving Cars: Fusing Deep Learning and Geometric Modeling, arXiv preprint, arXiv:1612.06573v1, Dec 2016.

Tian, Y., Pei, K., Jana, S. & Ray, B., DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars, IEEE/ACM 40th International Conference on Software Engineering (ICSE), pp. 303-314, 2018.

Krizhevsky, A., Sutskever, I. & Geoffrey, H. E., ImageNet Classification with Deep Convolutional Neural Networks, Adv. Neural Inf. Process. Syst., 25, pp. 1-9, 2012.

He, K., Zhang, X., Ren, S. & Sun, J., Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770-778, 2016.

Girshick, R., Fast R-CNN, Proc. IEEE Int. Conf. Comput. Vis., pp. 1440-1448, 2015.

Ren, S., He, K., Girshick, R. & Sun, J., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), pp. 1137-1149, 2017.

Liu, W., Anguelov, D., Erhan, D. Szegedy, C., Reed, S., Fu, C.Y. & Berg, A.C., SSD: Single Shot MultiBox Detector, arXiv preprint arXiv:1512.02325, Dec. 2015.

Redmon, J., Divvala, S., Girshick, R. & Farhadi, A., You Only Look Once: Unified, Real-time Object Detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788, 2016.

Deng, J., Dong, W., Socher, R., Li, L., Li, K. & Fei-Fei, L., ImageNet: A Large-scale Hierarchical Image Database, IEEE Conference on Computer Vision and Pattern Recognition, pp. 248-255, 2009.

Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J. & Zisserman, A., The Pascal Visual Object Classes (VOC) Challenge, Int. J. Comput. Vis., 88(2), pp. 303-338, 2009.

Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P. & Zitnick, C.L. Microsoft COCO: Common Objects in Context, Computer Vision – ECCV 2014, Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds), Lecture Notes in Computer Science, Vol. 8693, Springer Cham, 2014.

Stallkamp, J., Schlipsing, M., Salmen, J. & Igel, C., The German Traffic Sign Recognition Benchmark: A Multi-class Classification Competition, Proceedings of the International Joint Conference on Neural Networks, pp. 1453-1460, 2011.

Geiger, A., Lenz, P., Stiller, C. & Urtasun, R., Vision Meets Robotics: The KITTI Dataset, Int. J. Rob. Res., 32(11), pp. 1231-1237, 2013.

Ros, G., Sellart, L., Materzynska, J., Vazquez, D. & Lopez, A.M., The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3234-3243, 2016.

Dominguez-Sanchez, A., Cazorla, M. & Orts-Escolano, S., A New Dataset and Performance Evaluation of a Region-based CNN for Urban Object Detection, Electron., 7(11), 301, 2018.

Landis, J.R. & Koch, G.G., The Measurement of Observer Agreement for Categorical Data, Biometrics, 33, pp. 159-174, 1977.

Khosravy, M., Gupta, N., Marina, N. & Member, S., Perceptual Adaptation of Image Based on Chevreul – Mach Bands Visual Phenomenon, IEEE Signal Process. Lett., 24(5), pp. 594-598, 2017.

Udacity, Inc., An Open Source Self-Driving Car, Udacity, https://github.com/udacity/self-driving-car (10 November 2020).

Larsson, F. & Felsberg, M., Using Fourier Descriptors and Spatial Models for Traffic Sign Recognition, Springer: Berlin, pp. 238-249, 2011.

He, K., Zhang, X, Ren, S. & Sun, J., Spatial Pyramid Pooling in Deep Convolutional Networks, IEEE Trans. Pattern Anal. Mach. Intell., 37(9), pp. 1904-1916, 2015.

Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B. & Belongie, S., Feature pyramid networks for object detection, IEEE Conf. Comput. Vis. Pattern Recognit., pp. 936-944, 2017.

Ren, S., He, K., Girshick, R. & Sun, J., Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Trans. Pattern Anal. Mach. Intell., 39(6), pp. 1137-1149, 2017.

Redmon, J. & Farhadi, A., YOLO9000: Better, Faster, Stronger, 30th IEEE Conf. Comput. Vis. Pattern Recognition, pp. 6517-6525, 2017.

Redmon, J. & Farhadi, A., YOLOv3: An Incremental Improvement, arXiv preprint, arXiv:1804.02767, 2018.

Bochkovskiy, A., Wang, C.Y. & Liao, H.Y.M., YOLOv4: Optimal Speed and Accuracy of Object Detection, arXiv preprint, arXiv: 2004.10934, 2020.

Lin, T.Y., Goyal, P., Girshick, R., He, K. & Dollar, P., Focal Loss for Dense Object Detection, IEEE Int. Conf. Comput. Vis., pp. 2999-3007, 2017.

Strickland, M., Fainekos, G. & Ben-Amor, H., Deep Predictive Models for Collision Risk Assessment in Autonomous Driving, IEEE Int. Conf. Robot. Autom., pp. 4685-4692, 2018.

Mandal, V., Mussah, A.R., Jin, P. & Adu-gyamfi, Y., Sustainability Artificial Intelligence-enabled Traffic Monitoring System, Sustainability, 12(21), 9177, 2020.

Mulyanto, A., Borman, R.I., Prasetyawan, P., Jatmiko, W., Mursanto, P. and Sinaga, A., Indonesian Traffic Sign Recognition For Advanced Driver Assistent (ADAS) Using YOLOv4, 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), pp. 520-524, 2020.

Elyasi-Pour, R., Simulation Based Evaluation of Advanced Driver Assistance Systems, Thesis, Department of Science and Technology, Linköping University, Sweden, 2015.

Aranjuelo, N., Unzueta, L., Arganda-Carreras, I. & Otaegui, O., Multimodal Deep Learning for Advanced Driving Systems, Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 10945 LNCS, pp. 95-105, 2018.

Ball, J.E. & Tang, B., Machine Learning and Embedded Computing in Advanced Driver Assistance Systems (ADAS), Electron., 8(7), pp. 2-5, 2019.

Di Eugenio, B. & Glass, M., Squibs and Discussions: The Kappa Statistic: A Second Look, Comput. Linguist., 30(1), pp. 95-101, 2004.

Passonneau, R.J. & Carpenter, B., The Benefits of a Model of Annotation, The 7th Linguistic Annotation Workshop & Interoperability with Discourse, pp. 187-195, 2013.

Tang, W., Hu, J., Zhang, H., Wu, P. & He, H., Kappa Coefficient: A Popular Measure of Rater Agreement, Shanghai Arch. psychiatry, 27(1), pp. 62-67, 2015.

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Published

2021-03-31

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