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
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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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Abstract
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