Hoisting safe distance detection method based on deep learning

A technology of safety distance and detection method, applied in the directions of transportation and packaging, load hoisting components, cranes, etc., can solve the problems of hoisting safety distance detection, inability to judge the actual distance between workers and hooks, etc., to solve the difficulty of identification and positioning. Effect

Inactive Publication Date: 2018-12-18
DALIAN UNIV OF TECH
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Problems solved by technology

However, it is impossible to judge the actual distance between the worker and the hook, and it is impossib...

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  • Hoisting safe distance detection method based on deep learning
  • Hoisting safe distance detection method based on deep learning
  • Hoisting safe distance detection method based on deep learning

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[0035] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the implementation of the present invention. example, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0036] Such as figure 1 A hoisting safety distance detection method based on deep learning is shown, which is characterized in that it includes the following steps:

[0037] Step 1. Use the existing monitoring system on the tower crane to obtain the image inside the camera at the position of the boom just above the hook;

[0038] Step 2. Mark the workers and h...

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Abstract

The invention discloses a hoisting safe distance detection method based on deep learning. The hoisting safe distance detection method based on deep learning includes the steps that the image around ahoisting hook in a tower crane structure is obtained through a camera; a worker and the hoisting hook in the image are marked, and a data set are made; the data set is trained through Faster R-CNN indeep learning; the worker and the hoisting hook in the image are recognized and positioned through a trained detection model; the pixel distance between the worker and the hoisting hook is worked outaccording to positioning information in the detection result; and the pixel distance between the worker and the hoisting hook is converted into the actual distance between the worker and the verticalprojection point of the hoisting hook according to the height of the hoisting hook and the camera and the actual length and pixel length of the hoisting hook, and then detection of the hoisting safe distance is realized. By means of the hoisting safe distance detection method based on deep learning, recognition and positioning problems of the worker and the hoisting hook in the image are effectively solved, and high accuracy is achieved on the aspect of safe distance detection.

Description

technical field [0001] The invention belongs to the field of safety monitoring of civil engineering construction machinery, and relates to a method for detecting a safety distance between a worker and a hook during the hoisting process of a tower crane. More precisely, the present invention relates to a method that can identify and locate the worker and the hook in the image through a deep learning algorithm during the hoisting process, and realize the detection of the safe distance between the worker and the hook according to the positioning information and the conversion method. . Background technique [0002] With the continuous increase of urban population, the demand for high-rise and super high-rise buildings is increasing. In order to improve construction mechanization and industrialization, tower cranes are widely used in the construction process of high-rise and super high-rise buildings. The objects hoisted by the tower crane have high potential energy, wide opera...

Claims

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Application Information

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IPC IPC(8): B66C13/16B66C23/88
Inventor 赵雪峰张阳张明媛杨震
Owner DALIAN UNIV OF TECH
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