Method and system for detecting illegal slag car based on deep learning

A technology of deep learning and detection methods, applied in the direction of neural learning methods, instruments, biological neural network models, etc., can solve problems such as unsatisfactory effects, damage to the good image of the city, time-consuming and labor-intensive efficiency, etc., to maintain a good image and simple detection methods easy effect

Pending Publication Date: 2021-08-20
苏州市吴江区平望镇人民政府 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] In recent years, with the construction of civilized cities, various places have been constantly improving the construction of construction waste disposal sites. However, there are still people who are lucky enough to dump construction waste at will, destroying the good image of the city, and manual detection of illegal muck trucks is not only time-consuming It is laborious and inefficient, and the effect is not ideal, and there are still many fish that slip through the net
[0003] Most of the existing control methods for muck trucks are mainly managed by installing positioning devices on the muck trucks on the construction site. This method has a certain restraint effect on the muck trucks in local construction; Dirt trucks cannot be managed and controlled because they are not registered locally; in fact, these foreign muck trucks are often the ones that damage the environment, so how can these foreign muck trucks that have no local records be controlled Effective management becomes more important

Method used

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  • Method and system for detecting illegal slag car based on deep learning
  • Method and system for detecting illegal slag car based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0034] like figure 1 As shown, a depth study-based illegal slag breast detection method is presented, including the following specific steps:

[0035] S1, the data information of the captured camera is collected, and the geographic location information of the camera is positioned on the electronic map, and the vehicle information A captured by the camera is obtained.

[0036] Among them, information of the collected cameras includes, but is not limited to, the type information of the camera, the use of information, maintenance information, and the trailer information on the map; the camera and map latitude and latitude of the identified capture of the information are identified, and the access information is performed in real time. Transmission and information storage, through real-time data streaming to ensure the timeliness of data docking;

[0037] S2, classify the vehicle information A to obtain a slagbed transport set B;

[0038] S3, in accordance with whether the slag breast...

Embodiment 2

[0049] like figure 2 As shown, a depth study-based illegal slagburizing system, including

[0050] Vehicle capture module, used to capture vehicles on the road to obtain vehicle information A;

[0051] It should be noted that the vehicle capture module includes camera information entry and maintenance, camera positioning management, camera picture data docking, data forwarding, and data storage, where the camera information entry and maintenance mainly completes the entry of the camera base information, and corresponding information change maintenance, The camera positioning management mainly completes the latitude and longitude positioning of the camera on the map, and the actual position of the camera corresponds to the position on the electronic map, and the data docking mainly completes the acquisition of the camera, which is mainly implemented by the VPN internal network transmission mechanism. Data forwarding is to forward the acquired vehicle picture information to the vehi...

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Abstract

A deep learning-based illegal muck truck detection method comprises the following specific steps: S1, collecting data information of a camera on a road, positioning geographic position information of the camera on an electronic map, and obtaining vehicle information A captured by the camera; s2, classifying and recognizing the vehicle information A to obtain a muck vehicle information set B; s3, classifying slag cars in the slag car information set B; s4, according to the snapshot time and the positioning place of the muck truck, predicting the driving route of the muck truck and whether the muck is illegally poured; and S5, if it is predicted that the muck truck has the intention of illegal muck dumping, alarm reminding is carried out, and early warning information is sent to a mobile terminal of a processor. The invention further provides an illegal slag car detection system based on deep learning. According to the invention, manual work can be replaced to efficiently and accurately complete illegal muck truck detection, and the occurrence of an illegal muck dumping event of the muck truck is completely eradicated.

Description

Technical field [0001] The present invention relates to the field of computer depth learning, and more particularly to a depth study-based illegal slag mucastic detection method and system. Background technique [0002] In recent years, with the construction of civilized cities, all localities are constantly improving construction waste dispensers. However, there are still people who are lucky to pour the construction waste, destroy the city's good image, and through artificial detection of illegal saga, but not only time The labor efficiency is low, and the effect is not satisfactory, there is still a lot of fish. [0003] The existing Most Slag Pipeline control method is mainly managed by the mounting device for the mounting device of the construction site, this method has a certain constrained effect on the residual soil car of the local construction; but the slag of the slag Because of the local registration, it is not possible to control it; and the fact that the environment...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06K9/00G06N3/08
CPCG06N3/08G06V20/584G06F18/24
Inventor 戴丹杨晴华陈建林胡琴芳黄建鹏
Owner 苏州市吴江区平望镇人民政府
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