Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection

Abstract

The Internet of Things (IoT), including sensors, computer vision (CV), robotics, and visual reality technologies, is widely used in the construction industry to facilitate construction management in productivity and safety control. The application of such technologies in real construction projects requires high-quality computing resources, the network for data transferring, a near real-time response, geographical closeness to the smart environments, etc. Most existing research has focused on the first step of method development and has neglected the further deployment step. For example, when using CV-based methods for construction site monitoring, internet-connected cameras must transmit large quantities of high-quality data to the central office, which may be located thousands of miles away. Not only the quality may suffer due to latency, but the wideband cost can be astronomical. Edge computing devices and systems help solve this problem by providing a local source to process the data. The goal of this study is to embed the CV-based method into devices and thus to develop a practical edge computing system for vision-based construction resource detection, which can provide automatic construction with high-quality and more applicable service. Specifically, this study first developed a CV-based hardhat color detection model to manage workers in different tasks. Then, the model was embedded into a Raspberry Pi microcomputer mainboard for video data processing, and the performance was compared with the local computer to validate the feasibility of the proposed method.

Vision-Based Construction Safety Monitoring Utilizing Temporal Analysis to Reduce False Alarms

“…where TP denotes true positive, TN indicates true negative, FP represents false positives, and FN denotes false negatives in Equations ( 9) and (10). In Equation (11), n is the threshold level belonging to real numbers, and the values are in the range of 0 to 1, whereas N denotes the total number of classes.…”

“…Precision (also known as positive predictive value) ensures the accuracy of the positive predictions, and specificity (commonly referred to as the true negative rate) evaluates the ability of the model to identify negative instances correctly [91]. The precision and recall were calculated using the formulas expressed in Equations ( 9) and (10). The remaining metrics were calculated as follows [87]:…”

“…Nevertheless, the utmost responsibility of construction companies lies in ensuring the safety of their workers. Consequently, it is crucial to speed up processes, improve productivity, and deal with safety problems in the construction industry promptly [9][10][11][12][13].…”