Derin Evrişimli Sinir Ağlarını Kullanarak Araç Konumu Tahmini ve Araç Sınıflandırması

Yıl 2021, , 11 - 28, 30.06.2021

Bashaer Isam Hasan Kabeayla , Yasa Ekşioğlu Özok

https://doi.org/10.53600/ajesa.891207

Öz

Vehicle Position Estimation and Vehicle Classification Using Deep Convolutional Neural Networks

Öz

The aim of this paper is to classify the vehicles and estimate the position with license plate localization using deep convolutional Neural Network (DCNN). Vehicle pose estimation with license plate localization serves as one of the most widely-used real-world applications in fields like toll control, traffic scene analysis, and suspected vehicle tracking. We proposed a one-stage anchor-free vehicle classifier for simultaneously localizing the region of license plates and vehicles’ poses. The classifier, rather than bounding rectangles, gives bounding quadrilaterals, which gives a more precise indication for vehicle pose estimation with license plates localization. For single scale input, we reached mean Precision Accuracy mAP/mAP50 of 35.4/82.3 on the LISA benchmark dataset, already outperformed the existing commercial systems OpenALPR and Sighthound. For multi-scale input, we reached the best mAP/mAP50 of 40.8/90.1. For the vehicle pose (front-rear), classification accuracy reached 98.8%, average IoU reached 71.3%, giving a promising result as an end-to-end vehicle position estimation and license plate localization with contextual information. The work has performed in python programming language with several libraries of deep learning were being used for this purpose. Our DCNN model training started from an initial weight which we had already trained for about 110000 iterations in the model without classification head, so the total training iterations will be around 780000 including the transfer learning part in DCNN. Transfer learning made the DCNN model start at a smart point and made it easier to optimize all of the functional heads simultaneously.

Anahtar Kelimeler

Vehicle classification, pose estimation, optimization, DCNN, transfer learning, license plate, localization, deep learning

Kaynakça

• A. Newell, K. Yang and J. Deng, “Stacked hourglass networks for human pose estimation,” in The European Conference on Computer Vision (ECCV), 2016.
• Avalibale Online: https://www.openalpr.com
• Avalilable Online: https://www.sighthound.com/press/sighthound-ai-software-now-reads-license-plates.
• C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi and A. C. Berg, “Dssd: deconvolutional single shot detector,” arXiv preprint arXiv:1701.06659, 2017.
• H. Law and J. Deng, “CornerNet: detecting vehicles as paired keypoints,” in The European Conference on Computer Vision (ECCV), 2018.
• J. Redmon and A. Farhadi, “YOLOv3: an incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
• K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
• K. He, X. Zhang, S. Ren and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
• K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in International Conference on Learning Representations (ICLR), 2015.
• K. He, “The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results,” [Online]. Available: http://www.pascal network.org/challenges/VOC/voc2012/workshop/index.html.
• R. Girshick, “Fast R-CNN,” in IEEE International Conference on Computer Vision (ICCV), 2015.
• R. Girshick, J. Donahue, T. Darrell and J. Malik, “Rich feature hierarchies for accurate vehicle detection and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
• S. Du, M. Ibrahim, M. Shehata and W. Badawy, “Automatic License Plate Recognition (ALPR): a state-of-the-art review,” IEEE Transactions on Circuits and Systems for Video Technology 23(2), pp. 311-325, 2013.
• S. Ren, K. He, R. Girshick and J. Sun, “Faster R-CNN: towards real-time vehicle detection with region proposal networks,” in Conference on Neural Information Processing Systems (NIPS), 2015.
• S. Silva and C. Jung, “Real-time Brazilian license plate detection and recognition using deep convolutional neural networks,” in 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 2017.
• Sayanan Sivaraman and Mohan M. Trivedi, "A General Active Learning Framework for On-road Vehicle Recognition and Tracking," IEEE Transactions on Intelligent Transportation Systems, 2010. (pdf)
• T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan and C. L. Z. P. Dollár, “Microsoft COCO: Common Vehicles in Context,” in The European Conference on Computer Vision (ECCV), 2014.
• T.-Y. Lin, P. Goyal, R. Girshick, K. He and P. Doll´ar, “Focal loss for dense vehicle detection,” in IEEE International Conference on Computer Vision (ICCV), 2017.
• W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Fu and A. Berg, “SSD: single shot multibox detector,” in The European Conference on Computer Vision (ECCV), 2016.