Edge Detection and Identification using Deep Learning to Identify Vehicles
Subject Areas : Machine learning
Zohreh Dorrani
1
,
Hassan Farsi
2
,
Sajad Mohammadzadeh
3
*
1 - Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
2 - Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
3 - Department of Electrical and Computer Engineering, University of Birjand, Birjand, Iran
Keywords: Deep Convolution Neural Network, Edge Detection, Gaussian Mixing Method, Vehicle Detection.,
Abstract :
A deep convolution neural network (CNN) is used to detect the edge. First, the initial features are extracted using VGG-16, which consists of 5 convolutions, each step is connected to a pooling layer. For edge detection of the image, it is necessary to extract information of different levels from each layer to the pixel space of the edge, and then re-extract the feature, and perform sampling. The attributes are mapped to the pixel space of the edge and a threshold extractor of the edges. It is then compared with a background model. Using background subtraction, foreground objects are detected. The Gaussian mixture model is used to detect the vehicle. This method is performed on three videos, and compared with other methods; the results show higher accuracy. Therefore, the proposed method is stable against sharpness, light, and traffic. Moreover, to improve the detection accuracy of the vehicle, shadow removal conducted, which uses a combination of color and contour features to identify the shadow. For this purpose, the moving target is extracted, and the connected domain is marked to be compared with the background. The moving target contour is extracted, and the direction of the shadow is checked according to the contour trend to obtain shadow points and remove these points. The results show that the proposed method is very resistant to changes in light, high-traffic environments, and the presence of shadows, and has the best performance compared to the current methods.
[1] Z. Qu, SY. Wang, L. Liu, and DY. Zhou, "Visual Cross-Image Fusion Using Deep Neural Networks for Image Edge Detection", IEEE Access., Vol. 7, No. 1, 2019, pp. 57604-57615.
[2] Z. Dorrani, and M. S. Mahmoodi, "Noisy images edge detection: Ant colony optimization algorithm", Journal of AI and Data Mining, Vol. 4, No. 1, 2016, pp. 77-83.
[3] SM. Ismail, LA. Said, AG. Radwan, and AH Madian. "A novel image encryption system merging fractional-order edge detection and generalized chaotic maps, Signal Processing, Vol. 167, No.1, 2020, pp. 107280.
[4] N. Balamuralidhar, S. Tilon, and Francesco Nex. "MultEYE: Monitoring System for Real-Time Vehicle Detection, Tracking and Speed Estimation from UAV Imagery on Edge-Computing Platforms", Remote Sensing Vol. 13, No. 4, 2021, pp. 573.
[5] B. Wang, LL. Chen, ZY. Zhang, "A novel method on the edge detection of infrared image", Optik, Vol. 180, No.1, 2019, pp. 610-614.
[6] S. Choudhury, S.P. Chattopadhyay, and T.K. Hazra, "Vehicle detection and counting using haar feature based classifier", In receding’s of the 8th Annual Industrial Automation and Electromechanical Engineering Conference, 2017, Vol. 8, pp. 106–109.
[7] J. Sang, Z. Wu, P. Guo, H. Hu, H. Xiang, Q. Zhang, Cai, B. "An Improved YOLOv2 for Vehicle Detection", Sensors, Vol.18, No. 12, 2018, pp. 4272.
[8] W.U. Qiong, and L. Sheng-bin, "Single Shot MultiBox Detector for Vehicles and Pedestrians Detection and Classification", in 2nd International Seminar on Applied Physics, Optoelectronics and Photonics Inc. Lancaster, 2017, Vol. 2, pp. 1-7.
[9] S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards real-time object detection with region proposal networks", IEEE Trans. Pattern Anal, Vol. 39, No.6, 2017, pp. 1137–1149.
[10] A. Dubey, and S. Rane, "Implementation of an intelligent traffic control system and real time traffic statistics broadcasting", In Proceedings of the International conference of Electronics, Communication and Aerospace Technology, 2017, Vol.1, pp. 33–37.
[11] SM. Bhandarkar, Y. Zhang, and WD. Potter, "An edge detection technique using genetic algorithm-based optimization", Pattern Recognit, Vol. 27, No. 9, 1994, pp.1159–1180. [12] R. Chaudhary, A. Patel, S. Kumar, and S. Tomar, "Edge detection using particle swarm optimization technique", International Conference on Computing, Communication and Automation IEEE, 2017, Vol.1, pp.363–367.
[13] DS. Lu, CC. Chen, "Edge detection improvement by ant colony optimization", Pattern Recognit Lett. Vol. 9, No. 4, 2008, pp. 416–425.
[14] S. Xie, and Z. Tu. "Holistically-nested edge detection", In Proceedings of the IEEE international conference on computer vision, 2015, Vol. 125, pp. 3–18.
[15] S. Rajaraman, and A. Chokkalinga, "Chromosomal edge detection using modified bacterial foraging algorithm", Int J Bio-Science Bio-Technology, Vol. 6, No. 1, 2014, pp. 111–122.
[16] OP. Verma, M. Hanmandlu, AK. Sultania, and AS. Parihar, "A novel fuzzy system for edge detection in noisy image using bacterial foraging", Multidimens Syst Signal Process, Vol. 24, No. 1, 2013, pp.181–198.
[17] ME. Yüksel, "Edge detection in noisy images by neuro-fuzzy processing", Int J Electroni Commun, Vol. 61, No. 2, 2007, pp. 82–89.
[18] M. Setayesh, M. Zhang, and M. Johnston, "Edge detection using constrained discrete particle swarm optimization in noisy images", IEEE Congress of Evolutionary Computation (CEC), 2011, pp. 246–253.
[19] Y. Wang, Y. Li, Y. Song, and X. Rong, "The influence of the activation function in a convolution neural network model of facial expression recognition", Applied Sciences, Vol. 10. No. 5, 2020, pp. 1897.
[20] A. Sezavar, H. Farsi, and S. Mohamadzadeh. "Content-based image retrieval by combining convolutional neural networks and sparse representation", Multimedia Tools and Applications, Vol. 78, No. 15, 2019, pp. 20895-20912.
[21] Z. Song, L. Fu, J. Wu, Z. Liu, R. Li, and Y. Cui, "Kiwifruit detection in field images using Faster R-CNN with VGG16", IFAC-Papers On Line, Vol. 52, No. 30, 2019, pp. 76-81.
[22] S. Manali, and M. Pawar, "Transfer learning for image classification", Second International Conference on Electronics, Communication and Aerospace Technology, 2018, Vol. 2, pp. 656-660.
[23] W. Long, X. Li, and L. Gao, "A transfer convolutional neural network for fault diagnosis based on ResNet-50", Neural Computing and Applications, Vol. 32, No. 1, 2019, pp. 1-14.
[24] P. Ghosal, L. Nandanwar, and S. Kanchan, "Brain tumor classification using ResNet-101 based squeeze and excitation deep neural network", Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE, 2019, Vol.2, pp. 1-6.
[25] A. Sezavar, H. Farsi, and S. Mohamadzadeh. "A modified grasshopper optimization algorithm combined with CNN for content based image retrieval", International Journal of Engineering 32.7, 2019, 924-930.
[26] R. Nasiripour, H. Farsi, S. Mohamadzadeh. "Visual saliency object detection using sparse learning", IET Image Processing, Vol. 13, No. 13, 2019, pp. 2436-2447.
[27] H. Zamanian, H. Farsi, and S. Mohamadzadeh. "Improvement in Accuracy and Speed of Image Semantic Segmentation via Convolution Neural Network Encoder-Decoder", Information Systems & Telecommunication, Vol. 6, No. 3, 2018, pp. 128-135.
[28] R. Guerrero-Gomez-Olmedo, and RJ Lopez-Sastre, "Vehicle tracking by simultaneous detection and viewpoint estimation. "International Work-Conference on the Interplay Between Natural and Artificial Computation", Springer, 2013, pp. 306-316.
[29] Y. Wang, P.M. Jodoin, F. Porikli, J. Konrad, Y. Benezeth, and P. Ishwar, "CDnet 2014: An expanded change detection benchmark dataset", inProc. IEEE Conf. Comput. Vis. Pattern Recognit. Workshops, 2014, pp. 393–400.
[30] Z. Dorrani, H. Farsi, and S. Mohamadzadeh. "Image Edge Detection with Fuzzy Ant Colony Optimization Algorithm", International Journal of Engineering, Vol. 33, No. 12, 2020, pp. 2464-2470.
[31] D. Impedovo, F. Balducci, V. Dentamaro, and G. Pirlo, "Vehicular traffic congestion classification by visual features and deep learning approaches: a comparison", Sensors, Vol. 19, No. 23, 2019, pp. 5213-5225.
[32] C. Wen, P. Liu, W. Ma, Z. Jian, C. Lv, and J. Hong, "Edge detection with feature re-extraction deep convolutional neural network", Journal of Visual Communication and Image Representation, Vol. 57, No. 1, 2018, pp. 84-90.