Container Number Recognition Method Based on SSD_MobileNet and SVM
Keywords:SVM, Affine transformation, SSD_MobileNet, Container number recognition, Image processing
Aiming at how to realize the recognition of the container number on the container surface at the entrance and exit of the port, a method based on image affine transformation and SVM classifier is proposed. The main process includes truck target detection, box number area detection, text correction stage, image preprocessing stage and segmentation detection and recognition stage. Firstly, a kind of container truck detection program based on frame difference method and decreasing sequence of connected domain is proposed; secondly, a method of container number area detection based on SSD_MobileNet is proposed; in the case number recognition stage, a text correction method based on image affine transformation is proposed, and different processing methods are proposed for vertical sequence box number and horizontal sequence box number in image preprocessing stage In the stage of segmentation detection and recognition, a character segmentation algorithm based on connected domain segmentation and a segmentation detection and recognition algorithm based on SVM classifier are proposed. Through the detection and recognition of container images in the field monitoring video, the accuracy rate of regional detection can reach 97%, and the accuracy rate of character recognition can reach 95%, and it can achieve good real-time performance.
. T. Thurston and H.S Hu. “Distributed agent architecture for port automation,” Proc of the 26th Annual International Computer Software and Applications Conference, pp. 81-87, 2002.
. B.J. VICENT. “Multi-agent system technology in a port container terminal automation,” ERCIM News, pp. 37-39, 2004.
. B. Li and J.Q Yang. “A Scheduling Algorithm for Container Terminals within PID Control Framework,” Journal of Transportation Systems Engineering and Information Technology, pp.124-130, 2014.
. K. Itsuro, M. Mike and I. Takanobu. “SC-1 Introduction to practical AI image processing and analysis without programming,” Microscopy, vol. 68, pp. i11, Nov. 2019.
. S.W. Shi, X.Z. Zhang and Y.F. Wang. “Edge Computing: State-of-the-Art and Future Directions,” Journal of Computer Research and Development, pp. 73-93, 2019.
. J. Sun and L. Qian. “Research on Intelligent Video Surveillance Based on the Edge Computing Model, ” Computer & Digital Engineering, 2019.
. W. Liu, D. Anguelov and D. Erhan. “SSD: Single Shot MultiBox Detector,” European Conference on Computer Vision. Springer International Publishing, 2016.
. A.G. Howard, M. Zhu and Chen B. “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” 2017.
. K. Wu, D.P. Huang and S.J. Liu. “Skew correction of text image based on Radon transform,” Automation and information engineering, pp. 17-21, 2013.
. G.W. Zhou, X.J. Ping and J. Cheng. “Skew correction method for text image based on improved Hough transform,” Computer applications, vol. 27, pp. 1813-1816 , 2007.
. Z.L. Dong. “Fault diagnosis of power transformer based on multi-layer SVM classifier,” Electric Power Systems Research, 2005.
. B. Hu and C.X. Zhao. “An HOG-LGBPHS human detector with dimensionality reduction by PLS,” Pattern Recognition and Image Analysis, vol. 24, pp. 36-40, 2014.
. N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), vol. 1, pp. 886-893, 2005.
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