The transportation industry is accelerating its digital and intelligent transformation, and many regions continuously exploring the “smart” upgrade and transformation of toll stations. The application of target recognition technology in highway toll collection systems can effectively improve toll collection efficiency and accuracy. In view of the characteristics of monocular vision in existing recognition methods, this paper proposes a target recognition method for highway toll collection based on stereo vision, aiming to improve the accuracy and robustness of target recognition. Test results show that the designed method can achieve the recognition of human hand and high-speed ETC card or ETC payment code. Not only with high accuracy in target recognition, but also with good robustness, without the problem of a significant decrease in target recognition accuracy with an increase in the number of background target objects.
KEYWORDS: Information security, Fuzzy logic, Machine learning, Algorithms, Data processing, Transportation security, Neural networks, Network security, Information fusion, Complex systems
Information security risk assessment is the starting point and foothold of information security management. The construction of transportation informatization has achieved remarkable results, but the development of industry information security risk assessment is relatively backward, and the security level needs to be improved. While summarizing the typical information security risk assessment methods, this paper summarizes the existing advanced risk assessment methods at home and abroad, compares and analyzes the applicability of various methods in solving common problems in the process of industry information system risk assessment. The analysis results of this paper show that the information security risk assessment methods fused with multi-intelligent algorithms have better assessment effects, and provides a good reference for the choice of information security risk assessment methods in the transportation industry.
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