Container Number Recognition Method Based on SSD_MobileNet and SVM

Authors

  • Tang Chun-ming School of Artificial Intelligence Institute, Tiangong University, Tianjin, 300387, CHINA
  • chen Peng School of Electronics and Information Engineering, Tiangong University, Tianjin, 300387, CHINA

Keywords:

SVM, Affine transformation, SSD_MobileNet, Container number recognition, Image processing

Abstract

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.

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Published

2020-11-29

How to Cite

Chun-ming , T. ., & Peng , chen . (2020). Container Number Recognition Method Based on SSD_MobileNet and SVM. American Scientific Research Journal for Engineering, Technology, and Sciences, 74(1), 200–211. Retrieved from https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/6482

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Section

Articles