Bus Rapid Transit (BRT) has become an increasing source of concern for public transportation of modern cities. Traditional contact sensing techniques during the process of health monitoring of BRT viaducts cannot overcome the deficiency that the normal free-flow of traffic would be blocked. Advances in computer vision technology provide a new line of thought for solving this problem. In this study, a high-speed target-free vision-based sensor is proposed to measure the vibration of structures without interrupting traffic. An improved keypoints matching algorithm based on consensus-based matching and tracking (CMT) object tracking algorithm is adopted and further developed together with oriented brief (ORB) keypoints detection algorithm for practicable and effective tracking of objects. Moreover, by synthesizing the existing scaling factor calculation methods, more rational approaches to reducing errors are implemented. The performance of the vision-based sensor is evaluated through a series of laboratory tests. Experimental tests with different target types, frequencies, amplitudes and motion patterns are conducted. The performance of the method is satisfactory, which indicates that the vision sensor can extract accurate structure vibration signals by tracking either artificial or natural targets. Field tests further demonstrate that the vision sensor is both practicable and reliable.

Given the flammability of power cables and the high cost of utility tunnel construction, power cable fires cause serious economic losses and are associated with a negative social impact. In the study, a weighted fuzzy Petri net and an event tree are combined to propose a quantitative evaluation method to mitigate cable fire risks in a utility tunnel. First, cable fire risk factors are analyzed. Given the lack of utility tunnel cable fire historical data, fuzzy theory is used to calculate the failure probability of the primary event. Second, a weighted fuzzy Petri net is used for fuzzy reasoning, and an event tree is used to analyze all possible consequences. Subsequently, the numerical simulation method is used to quantify the loss from the cable fire and thereby quantify the risk of cable fire. Finally, the effect of different risk factors on a cable fire is analyzed to determine the main factors that affect cable fires. Simultaneously, the control ability of different control measures with respect to the fire is analyzed to determine key control measures. A case study of a utility tunnel cable cabin in Liupanshui in Guizhou is employed to validate the utility of the proposed method.

This paper studies the limitations of binocular vision technology in monitoring accuracy. The factors affecting the surface displacement monitoring of the slope are analyzed mainly from system structure parameters and environment parameters. Based on the error analysis theory, the functional relationship between the structure parameters and the monitoring error is studied. The error distribution curve is obtained through laboratory testing and sensitivity analysis, and parameter selection criteria are proposed. Corresponding image optimization methods are designed according to the error distribution curve of the environment parameters, and a large number of tests proved that the methods effectively improved the measurement accuracy of slope deformation monitoring. Finally, the reliability and accuracy of the proposed system and method are verified by displacement measurement of a slope on site.

The paper presents an intelligent real-time slope surface deformation monitoring system based on binocular stereo-vision. To adapt the system to field slope monitoring, a design scheme of concentric marking point is proposed. Techniques including Zernike moment edge extraction, the least squares method, and k-means clustering are used to design a sub-pixel precision localization method for marker images. This study is mostly focused on the tracking accuracy of objects in multi-frame images obtained from a binocular camera. For this purpose, the Upsampled Cross Correlation (UCC) sub-pixel template matching technique is employed to improve the spatial-temporal contextual (STC) target-tracking algorithm. As a result, the tracking accuracy is improved to the sub-pixel level while keeping the STC tracking algorithm at high speed. The performance of the proposed vision monitoring system has been well verified through laboratory tests.

Intelligent safety management based on machine vision has become indispensable in reducing collision safety accidents during construction. To prevent collisions between workers and machines in excavation site construction, a real-time intelligent evaluation system to reflect worker–machine safety status was developed. The system included: (1) determination of the key factors affecting the safety of the interactive operation between workers and machines; (2) extraction of precursor semantic information related to the safety assessment for each object in the construction site based on machine vision; and (3) assessment of the safety state of a monitored object using a fuzzy neural network. A case study of excavation site construction is presented to illustrate and verify the entire process of safety assessment using the developed framework. The results show that the proposed model achieves high detection rates: 96% and 94% for tracking accuracy and 91.67% for prediction accuracy.

Working at heights causes heavy casualties among workers during construction activities. Workers’ unsafe action detection could play a vital role in strengthening the supervision of workers to avoid them falling from heights. Existing methods for managing workers’ unsafe actions commonly rely on managers’ observation, which consumes a lot of human resources and impossibly covers a whole construction site. In this research, we propose an automatic identification method for detecting workers’ unsafe actions, considering a heights working environment, based on an improved Faster Regions with CNN features (Faster R-CNN) algorithm. We designed and carried out a series of experiments involving five types of unsafe actions to examine their efficiency and accuracy. The results illustrate and verify the method’s feasibility for improving safety inspection and supervision, as well as its limitations.