Monitoring method capable of identifying belt deviation through artificial intelligent video

A technology of video recognition and artificial intelligence, applied in the direction of conveyor control devices, conveyors, conveyor objects, etc., can solve time-consuming and labor-intensive security and stability issues

Active Publication Date: 2019-07-23
JINGYING SHUZHI TECH HLDG CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the defects of time-consuming, labor-intensive and weak security and stability in the prior art, the present invention proposes

Method used

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  • Monitoring method capable of identifying belt deviation through artificial intelligent video
  • Monitoring method capable of identifying belt deviation through artificial intelligent video
  • Monitoring method capable of identifying belt deviation through artificial intelligent video

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0022] Such as Figures 1 to 4 As shown, the artificial intelligence video recognition monitoring method of belt deviation in this embodiment first selects no less than two idler rollers 3 on both sides of the belt 2 as more than two tracking targets 4, and then uses a high-definition explosion-proof camera for mining. 1 Real-time collection of tracking target video, mining high-definition explosion-proof camera 1 transmits the tracking target video to the server in real time through the network, the server analyzes the tracking target video through the artificial intelligence video recognition model, and when the tracking target 4 is blocked, it detects whether it is blocked by the belt 2 In order to track the target 4, if the detection result is the occlusion caused by the belt 2, it is determined that the belt 2 is deviation. If the detection result is not the occlusion caused by the belt 2, it is not judged as the deviation of the belt 2.

[0023] Preferably, the tracking ...

Embodiment 2

[0031] Such as Figures 1 to 4 As shown, the artificial intelligence video recognition monitoring method for belt deviation in this embodiment first frames and selects no less than two outer ends of idler rollers 3 on both sides of the belt 2 as more than two tracking targets 4, and then passes the mining high-definition The explosion-proof camera 1 collects the video of the tracking target in real time, and the mine-used high-definition explosion-proof camera 1 transmits the video of the tracking target to the server in real time through the network. The server analyzes the video of the tracking target through the artificial intelligence video recognition model. When the tracking target 4 is blocked, it detects whether it is a belt 2 blocks the tracking target 4, and the detection result is that the belt 2 is blocked to determine the belt 2 deviation, and the detection result is not caused by the belt 2, it is not judged as the belt 2 deviation.

[0032] Preferably, the track...

Embodiment 3

[0040] Such as Figures 1 to 4 As shown, the artificial intelligence video recognition monitoring method for belt deviation in this embodiment first frames and selects no less than two outer ends of idler rollers 3 on both sides of the belt 2 as more than two tracking targets 4, and then passes the mining high-definition The explosion-proof camera 1 collects the video of the tracking target in real time, and the mine-used high-definition explosion-proof camera 1 transmits the video of the tracking target to the server in real time through the network. The server analyzes the video of the tracking target through the artificial intelligence video recognition model. When the tracking target 4 is blocked, it detects whether it is a belt 2 blocks the tracking target 4, and the detection result is that the belt 2 is blocked to determine the belt 2 deviation, and the detection result is not caused by the belt 2, it is not judged as the belt 2 deviation.

[0041] Preferably, the track...

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PUM

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Abstract

The invention discloses a monitoring method capable of identifying belt deviation through an artificial intelligent video. The monitoring method comprises the following steps of selecting not less than two support rollers on two side frames of a belt as two or more tracking objects, acquiring a tracking object video in real time through a mineral high-definition anti-explosion video camera, transmitting the tracking object video to a server in real time through a network by the mineral high-definition anti-explosion video camera, analyzing the tracking target video by the server through an artificial intelligent video identifying module, detecting whether the belt shields a tracking target or not when the tracking target is shielded, determining that the belt deviates if the detected result is that shielding is caused by the belt, and judging that the belt does not deviate if the detected result is that shielding is not caused by the belt. The invention aims to solve the belt operationdetecting problem through an artificial intelligent technology. The monitoring method achieves timely finding and alarming while a belt deviation phenomenon appears in a belt operation process, is suitable for various belt transportation scenes, and reduces manual timed correcting operation of an existing detecting sensor, so that coal mine operation safety is ensured, and waste of labor power and time is reduced.

Description

technical field [0001] The invention belongs to the field of belt transportation monitoring, and in particular relates to a monitoring method for belt deviation recognition by artificial intelligence video. Background technique [0002] Coal conveying belt conveyor is the main equipment for transporting materials in coal mines. During the long-term operation of the belt conveyor, belt deviation is a frequent failure. When the belt deviates to a certain extent, the belt will trigger the emergency stop device for deflection prevention, which will cause the shutdown of the operating system and affect the production process; cause abnormal damage to the main parts of the equipment; it is easy to form a safety hazard; due to the serious deviation of the belt, The belt turns over the material, causing the force on one side of the belt to exceed the longitudinal breaking force of the belt, which causes safety hazards such as lateral tearing of the belt. Therefore, it is extremely ...

Claims

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Application Information

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IPC IPC(8): B65G43/02
CPCB65G43/02B65G2203/0283B65G2203/041
Inventor 吴喆峰曹凌基朱晓宁
Owner JINGYING SHUZHI TECH HLDG CO LTD
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