Deep Learning-based Algorithm for Intelligent Sensing and Analysis of Urban Environment

This paper discusses the deep learning-based intelligent sensing and analysis system for urban environment, aiming to improve the efficiency of problem discovery in the business scenario of “urban inspection”. The traditional inspection model relies too much on the personal experience of the inspector, who needs to inspect the scene and determine the classification of the problem, which is prone to omission and subjective misjudgment, and it is difficult to ensure the comprehensiveness and timeliness of the inspection, resulting in inefficiency of the inspection, and low coverage of the problem, and other problems. In order to solve the inefficiency caused by human factors, and thus improve the problem discovery efficiency and accuracy of inspection, this paper proposes an innovative method to realize the intelligent identification of urban environmental problems by combining panoramic imaging technology, YOLOv8 and large models. The 360° omni-directional view of the panoramic image can comprehensively capture the urban environmental information, while the target detection technology can automatically identify and locate key targets in the image, and use the powerful semantic comprehension ability of the big model to qualitatively discriminate the problems. The urban inspection system constructed based on this method improves the inspection efficiency, and the correct recognition rate of the problem of “unclean around trash cans” reaches more than 87%, and the number of problems detected in a single day is increased by 133% compared with the old model. This research result provides a brand-new solution for urban inspection and a strong boost for the development of the smart city industry.