SkyVision Platform Achieves Remarkable Results in Perimeter Intrusion Detection

With the continuous development of the emergency security / intelligent security industry, the importance of perimeter intrusion detection has become increasingly prominent. DaoAI's SkyVision platform provides an efficient solution for this industry with its unique advantages.

Industry background and user scenario: The emergency security / intelligent security industry is an important field to safeguard people's lives and property. In some large-scale factories, logistics parks and other places, perimeter security is of crucial importance. A large-scale logistics park has a vast area with a large amount of goods stored around it, and there is frequent flow of people and vehicles. Its perimeter protection mainly relies on traditional surveillance cameras, but these cameras can only be used for post-event review and cannot detect and warn of perimeter intrusion behaviors in real-time, bringing great potential safety hazards to the park.

Pain points: Why is this hurdle so difficult to overcome?

Multi - dimensional quantification of pain points: Firstly, the false-negative rate is high. Under the traditional monitoring method, the false-negative rate of perimeter intrusion is over 20%, and many intrusion behaviors of lawbreakers cannot be detected in time. Secondly, the false-positive rate remains high. Due to factors such as the wind, the passing of animals, the false-positive rate is as high as 30%. This requires a large amount of manual re-judgment time for security personnel, and more than 3 hours are spent on re-judgment every day. Thirdly, the compliance risk is high. In some special periods, the requirements for perimeter security are higher. If intrusion cannot be effectively prevented, serious compliance penalties may be faced.

Root - cause analysis: From the process level, traditional monitoring is just a simple image record without advanced algorithms for analysis, so it is impossible to accurately identify intrusion behaviors. In terms of imaging, ordinary surveillance cameras have limited resolution. At night or in bad weather, the images are blurred, making it difficult to clearly capture intrusion features. In terms of materials, traditional equipment lacks intelligent processing capabilities. Moreover, the complex flow of people and vehicles in the park increases the difficulty of detection.

Technical principle

In - depth mechanism: The SkyVision zero-code video surveillance AI platform uses advanced deep-learning algorithms to analyze video images in real-time. It trains its own model on - site within hours, allowing the model to learn the characteristics of the perimeter environment and normal behavior patterns. At the same time, it uses the DaoAI World model for semantic understanding, enabling it to accurately distinguish different behaviors and events. In terms of hardware, it is equipped with an edge box to achieve real-time alarm functions.

Comparison with traditional methods: Compared with traditional rule-based AOI, the SkyVision platform does not require complex rule settings and can adapt to different environments and scenarios. Traditional manual visual inspection is inefficient and prone to fatigue, and cannot achieve 24-hour continuous monitoring. The SkyVision platform can detect intrusion behaviors in real-time and accurately, greatly improving the efficiency and accuracy of detection.

Typical application scenarios

• Fence climbing detection: Through real-time monitoring of the fence area, the model is used to identify climbing actions. The difficulty lies in accurately distinguishing normal climbing for maintenance and illegal climbing by people.
• Illegal entry detection: Monitor the entrances and exits and non-open areas of the perimeter to detect whether there are unauthorized people or vehicles entering. The difficulty lies in adapting to different lighting and weather conditions to ensure the accuracy of detection.
• Loitering detection: Identify people who loiter near the perimeter for a long time and judge whether they have intrusion intentions. The difficulty lies in distinguishing normal waiting and suspicious loitering behaviors.
• Unauthorized vehicle entry detection: Monitor the vehicle passages on the perimeter to detect whether there are unauthorized vehicles entering. The difficulty lies in accurately identifying the vehicle type and license plate information.

Implementation case

Comparison of an anonymous customer before and after implementation: After a large-scale logistics park introduced the SkyVision platform, the implementation process was smooth. Before the implementation, the false-negative rate of perimeter intrusion was 22%, the false-positive rate was 32%, and the manual re-judgment time was 3.5 hours per day. After the implementation, the false-negative rate was reduced to less than 1%, the false-positive rate was reduced to less than 5%, and the manual re-judgment time was reduced to less than 1 hour per day.

The SkyVision platform has brought a qualitative leap to perimeter intrusion detection in the emergency security industry.

DaoAI's solution and products

Product capabilities and implementation methods: Centered around the SkyVision platform, it has the advantage of zero-code operation, and users can use it without professional programming knowledge. In terms of modeling, it trains its own model on - site within hours to quickly adapt to different perimeter environments. When changing scenarios, it can quickly adjust model parameters to meet new scenario requirements. In terms of deployment, it supports 100% local privatization, and data does not leave the factory, ensuring data security. It can also be integrated with the existing monitoring system in the park. The supporting DaoAI World model provides semantic understanding support, enhancing the platform's intelligent analysis ability.

Quantitative results and business value: By using the SkyVision platform, the detection rate of perimeter intrusion has reached over 99%, the false-negative rate has been reduced to less than 1%, the false-positive rate has been reduced by -84.375%, and the manual re-judgment time has been reduced by -71.43%. This not only improves the security level of the park but also reduces labor costs, with significant business value.