Introduction to Abnormal Inspection Projects for Dump Truck Covers
Project Introduction
This product is a smart law enforcement solution tailored for the municipal management department of a certain city to detect abnormalities in dump truck covers. The solution utilizes artificial intelligence image/video recognition technology to analyze data in real-time from urban traffic monitoring platforms, identifying suspected abnormal images of dump truck covers, compiling data, and submitting it to relevant law enforcement departments for confirmation and punishment. This system empowers the municipal command center to identify and collect visible violations on the current road in real-time while indexing and matching vehicle information with a whitelist dump truck database, enabling timely punishment for violations and unregistered vehicles, and providing effective data support for action departments.
Data Processing Process
Supporting Server
Product: Intelligent Visual Analysis Node Server Brand: BillioTech Model: SHARP-80
Features:
1. Low cost, quick deployment
2. Soft and hard integrated customized environment
3. Local deployment SDK to ensure data security
4. Self-evolving intelligent recognition model
Interface Display
Actual Measurement Effects
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High recognition rate and fast speed - The algorithm for identifying illegal dump trucks that has been put into use far exceeds the algorithms of existing mature products (such as a certain company's algorithm). The recognition rate for illegal dump trucks reaches over 97%. It effectively helps law enforcement personnel efficiently filter out illegal dump trucks from tens of thousands of suspected targets daily.
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The algorithm evolves itself; as the municipal management department continues to use it, the ability to judge illegal dump trucks will improve with the number of judgments, thus continuously approaching human judgment levels.
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Flexible installation, real-time stability, and data security.
This project went live in November 2020 and has been running smoothly, successfully processing 2.5 million vehicle trips to date.