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<p>Traditional inspection methods for highways include fixed cameras and automatic detection systems. Although they automate the inspection process to a certain extent, they still face problems such as limited coverage, delayed data processing, and insufficient identification accuracy. These systems may face compatibility challenges when integrated with new technologies. To address these issues, this article explores the application of intelligent image identification technology in highway inspections. High-resolution cameras are used for data acquisition, and convolutional neural networks (CNN) are applied to extract image features and generate feature maps. The faster region-based convolutional neural network (Faster R-CNN) is used to classify and perform the bounding box regression on the features extracted by CNN in these regions, determining the category and precise location of the object. At the same time, the deep learning algorithm Autoencoder is used for anomaly detection, and multi-source data is integrated through Kalman filtering. The model parameters and computational graphs are also optimized to improve system efficiency. A 12-km-long highway was selected for testing. The average processing time of the system was within 400&#xa0;ms, and the processing time for each frame of the image was about 150&#xa0;ms. It can be seamlessly integrated with the existing system, meeting the real-time requirements. The results show that intelligent image recognition technology can achieve higher highway inspection efficiency and accuracy while maintaining good compatibility with existing equipment.</p>

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Application and optimization of intelligent image identification technology in highway inspection data

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Wenjuan Wang,
  • Chengwu Li,
  • Shouwei Sun,
  • Chao Shen,
  • Yunbin Wang

摘要

Traditional inspection methods for highways include fixed cameras and automatic detection systems. Although they automate the inspection process to a certain extent, they still face problems such as limited coverage, delayed data processing, and insufficient identification accuracy. These systems may face compatibility challenges when integrated with new technologies. To address these issues, this article explores the application of intelligent image identification technology in highway inspections. High-resolution cameras are used for data acquisition, and convolutional neural networks (CNN) are applied to extract image features and generate feature maps. The faster region-based convolutional neural network (Faster R-CNN) is used to classify and perform the bounding box regression on the features extracted by CNN in these regions, determining the category and precise location of the object. At the same time, the deep learning algorithm Autoencoder is used for anomaly detection, and multi-source data is integrated through Kalman filtering. The model parameters and computational graphs are also optimized to improve system efficiency. A 12-km-long highway was selected for testing. The average processing time of the system was within 400 ms, and the processing time for each frame of the image was about 150 ms. It can be seamlessly integrated with the existing system, meeting the real-time requirements. The results show that intelligent image recognition technology can achieve higher highway inspection efficiency and accuracy while maintaining good compatibility with existing equipment.