Abstract
Tower cranes are the most commonly used large-scale equipment on construction site. Because workers can’t always pay attention to the environment at the top of the head, it is often difficult to avoid accidents when heavy objects fall. Therefore, safety construction accidents such as struck-by often occurs. In order to address crane issue, this research recorded video data by a tower crane camera, labeled the pictures, and operated image recognition with the MASK R-CNN method. Furthermore, The RGB color extraction was performed on the identified mask layer to obtain the pixel coordinates of workers and dangerous zone. At last, we used the pixel and actual distance conversion method to measure the safety distance. The contribution of this research to safety problem area is twofold: On one hand, without affecting the normal behavior of workers, an automatic collection, analysis, and early-warning system was established. On the other hand, the proposed automatic inspection system can help improve the safety operation of tower crane drivers.
Highlights
Countries all over the world pay great attention to security issues due to the dynamic and complex work conditions of construction sites [1]
Mask R-convolutional neural network (CNN) can reduce the impact of pixel misalignment compared to other RCNN methods
The on-site hazard, hook, and workers are identified by Mask R-CNN, and the safety distance is converted pixel hazard, coordinate extraction method
Summary
Countries all over the world pay great attention to security issues due to the dynamic and complex work conditions of construction sites [1]. Construction workers’ safety situation is grim, as the quantity of accidents is still rising recent years. In 2017, both the quantity of safety accidents and the quantity of deaths have increased compared with 2016. Several dynamic factors such as wires carrying the load are dynamically stressed, causing the lift is so difficult to perform. Too many projects under construction lead to the large amount of accidents. These accidents reflect the problems of extensive safety management, inadequate safety protection, and weak safety awareness. Some countries have developed effective programs to improve Occupational Safety & Health (OSH) [3], lifting accidents are a scourge worldwide
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