Application of video recognition and dynamic monitoring technology based on deep learning: taking the rail transit construction project as an example

doi:10.13474/j.cnki.11-2246.2022.0258

Abstract

Abstract: Based on the current situation of safety management and informatization construction of urban rail transit construction projects at home and abroad,this paper analyzes the difficulties faced by safety management in the current rail transit construction process and discusses the video recognition method based on Faster R-CNN,which introduces image space features to improve the recognition rate of unsafe behavior. Also,this paper builds the potential strong correlation relationship between the construction environment,equipment dynamic monitoring and hazard data based on the Apriori,so as to improve the efficiency of hazard discovery and disposal.On this basis,the author proposes the architecture of the safety management platform for rail transit construction,and introduces the application practice of the core modules of safety management,including safety risk management and video monitoring.The results show that the algorithm and platform described in this paper can effectively improve the safety information processing efficiency,reduce the overall incidence rate of safety accidents,and provide effective technical support for the safety management of urban rail transit construction.

Key words: safety management, deep learning, correlation analysis, rail transit construction, information platform

CLC Number:

P208
P258

WU Zhenzhen, TANG Chao, YANG Xiaofei. Application of video recognition and dynamic monitoring technology based on deep learning: taking the rail transit construction project as an example[J]. Bulletin of Surveying and Mapping, 2022, 0(9): 23-28.

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