SkyVision Guards Perimeters, False Alarm Rate Reduced by -63%

In the field of emergency and smart security, especially for perimeter protection of large industrial parks, manufacturing bases, and critical infrastructure, traditional video surveillance systems face severe challenges. DaoAI SkyVision 0-code video surveillance AI platform (on-site hourly training of proprietary models, behavior/event recognition, edge box real-time alerting, 100% local data, DaoAI World semantic understanding) precisely identifies and semantically understands perimeter intrusion/overcoming behaviors in complex environments, reducing the false alarm rate of a large manufacturing base's perimeter security system from 18% to 6.7%, significantly improving security response efficiency and effectively reducing the burden of manual patrols and reviews. This achievement is due to SkyVision's ability to transform traditional 'qualitative' standards, which rely on human empirical judgment, into consistent, quantifiable machine vision standards, thereby enabling precise capture and efficient early warning of abnormal behaviors.

DaoAI SkyVision 0-code video surveillance AI platform (on-site hourly training of proprietary models, behavior/event recognition, edge box real-time alerting, 100% local data, DaoAI World semantic understanding) precisely identifies and semantically understands perimeter intrusion/overcoming behaviors in complex environments, reducing the false alarm rate of a large manufacturing base's perimeter security system from 18% to 6.7%, significantly improving security response efficiency and effectively reducing the burden of manual patrols and reviews. In the field of emergency and smart security, perimeter protection of large industrial parks, substations, data centers, and other critical infrastructure is paramount. These sites often cover vast areas with perimeters extending for kilometers, making traditional human patrols combined with basic video surveillance inadequate in facing complex and dynamic external environments and increasing security threats. Our client, a leading global electronics manufacturer, operates a super factory in East China covering thousands of acres, with a perimeter defense line spanning tens of kilometers and thousands of high-definition surveillance cameras. The factory demands extremely high security levels, as any unauthorized intrusion could lead to severe production disruptions, intellectual property leakage, or even personnel safety risks. The factory's security department has long faced significant challenges, especially in detecting perimeter intrusions and climbing behaviors at night, during adverse weather (e.g., heavy fog, rain), and in complex terrains (e.g., slopes, densely vegetated areas).

Pain Points: Why This Challenge Is So Difficult

The client's perimeter security system faced multiple pain points: First, **high false alarm rates**. Traditional video analytics systems based on motion detection or simple rules frequently generated a large number of false alarms due to factors like wind-blown vegetation, small animal activity, light changes, and adverse weather (rain, snow). This led to security personnel being constantly occupied with verification, with false alarm rates reportedly reaching 18%, severely diluting attention to genuine alerts. Second, **undetected risks**. Some experienced intruders could exploit blind spots or use camouflage to infiltrate, as traditional systems struggled to identify complex behavioral patterns, resulting in potential security vulnerabilities. Third, **high labor costs and delayed response**. To compensate for system deficiencies, the factory had to invest significant human resources in 24/7 video monitoring and patrols. Even with this, there was often a delay of several minutes or more from alarm trigger to personnel arrival for on-site verification, missing optimal intervention opportunities.

The root cause of these difficulties is that traditional security systems still define the 'qualitative' standards for perimeter intrusion, such as 'human silhouette' or 'climbing action,' in vague terms that rely on human empirical judgment. For instance, swaying leaves in the wind might be misidentified as a 'human silhouette' by traditional systems, while a person moving quickly and low to the ground over a fence might be missed in low-resolution or backlit conditions. Traditional algorithms struggle to differentiate the semantic nuances between 'normal environmental disturbances' and 'actual intrusion behaviors.' They lack a deep understanding of human posture, movement trajectories, and object-environment interactions in complex scenarios. The current industry trend emphasizes transforming human 'qualitative' standards into consistent, quantifiable machine standards. For perimeter security, this means breaking down the act of 'climbing a fence' into a series of features that can be precisely identified and quantified by machines, such as limb trajectories within specific areas, changes in center of gravity, contact points with the fence, and duration. Traditional systems cannot achieve this; their detection logic is often based on pixel-level changes or zone intrusion, failing to grasp the intent and context of behaviors, leading to poor performance in complex, dynamic environments.

Technical Principles

The core of the DaoAI SkyVision platform lies in its powerful 0-code AI model training capabilities and semantic understanding technology based on the DaoAI World model. Through our innovative APDT positive/few-shot learning mechanism, clients only need to provide 1–20 typical intrusion or climbing behavior images or short video clips as 'positive samples' on-site, and the system can complete proprietary model training within hours. This significantly surpasses the traditional machine learning requirement for large amounts of labeled data. At the algorithmic level, SkyVision integrates multi-modal feature extraction with temporal behavior analysis. It not only identifies target objects (people, vehicles) in images but, more importantly, uses deep learning networks to analyze the target's movement trajectory, posture changes, and interaction with perimeter obstacles (walls, fences) in real-time, building a spatio-temporal semantic map of behaviors. For example, for 'climbing' behavior, the system identifies a continuous sequence of actions: the target approaching the fence from the ground, climbing, body passing over the top, and finally landing, and determines if it matches the preset 'climbing' pattern. The DaoAI World model provides a unified AI foundation, giving SkyVision more advanced semantic understanding capabilities. It can distinguish between 'normal personnel walking near a fence' and 'attempting to climb a fence'—two seemingly similar but entirely different behaviors—effectively filtering out false alarms and continuously learning from production feedback to optimize model performance.

Compared to traditional methods, SkyVision's advantage is clear: traditional rule-based AOI systems for perimeter intrusion detection primarily rely on pixel changes, area intrusion, or line crossing detection, making threshold setting difficult and susceptible to environmental interference. Manual visual inspection is inefficient, prone to fatigue, and highly subjective. SkyVision, however, uses deep learning to transform the human eye's vague perception of 'abnormal behavior' into precise, quantifiable feature vectors, such as limb key points, motion vectors, and posture sequences. These features are processed in real-time on edge boxes, combined with DaoAI World's semantic understanding capabilities, enabling high-precision identification of complex behaviors like 'climbing,' 'scaling,' and 'illegal loitering.' This reduces false alarm rates to levels unattainable by manual means while ensuring high detection rates, truly achieving a leap from 'seeing' to 'understanding.'

Typical Application Scenarios

• **Perimeter Intrusion Detection**: Real-time monitoring for unauthorized entry of personnel or vehicles into designated alert zones in sensitive areas such as industrial parks, airports, and substations. The challenge lies in distinguishing non-threatening targets like small animals, birds, or swaying trees from actual intruders.
• **Wall/Fence Overcoming Detection**: Precisely identifying any attempts to climb or scale high walls or fences around factories, warehouses, and data centers. Challenges include varying heights and body types of individuals, different climbing postures, and detection under nighttime or backlit conditions.
• **Illegal Loitering/Lingering Detection**: Detecting whether individuals are lingering for extended periods or repeatedly loitering near the perimeter or within sensitive areas, potentially indicating reconnaissance or opportunistic intrusion. The difficulty is in distinguishing normal personnel (e.g., patrolling security guards, maintenance staff) from suspicious individuals, requiring comprehensive judgment based on time, area, and behavior patterns.
• **Abandoned Object Detection**: Detecting suspicious packages, tools, or other items left behind at the perimeter or critical entry points. The challenge is to exclude common environmental debris and identify potentially threatening objects.
• **Abnormal Gathering Detection**: Detecting an unusual concentration of personnel exceeding a preset number within sensitive perimeter areas, potentially indicating a group event or coordinated intrusion. The challenge is dynamic identification and counting, while excluding normal gatherings of staff.

Case Study

For a leading electronics manufacturer's super factory, perimeter security is a critical safety component. Before integrating DaoAI SkyVision, the factory's perimeter security system relied primarily on traditional video surveillance combined with manual monitoring and patrols. The security department received hundreds of alerts daily from the video surveillance system, with over 80% confirmed as false alarms after manual review. This not only consumed significant security personnel resources but also led to 'alert fatigue' among security staff, reducing their response speed to genuine threats. We collaborated closely with the client, first deploying SkyVision edge boxes in high-risk, false-alarm-prone perimeter areas. Using historical data from the factory and simulating a few intrusion behaviors on-site, we completed the training of proprietary models for 'perimeter intrusion' and 'fence climbing' behaviors within hours. In the initial phase, SkyVision operated in parallel with the existing system for data comparison and verification. After one month of trial operation and model iteration optimization, SkyVision achieved a detection rate of 99.4% for real intrusion behaviors, while the false alarm rate decreased from the previous 18% to 6.7%, a significant improvement. The average review time for security personnel was reduced by over 50%, allowing them to focus more on preventing actual security threats. More importantly, by effectively filtering out false alarms, the overall credibility of the security system was enhanced, enabling the security team to respond more quickly and accurately to potential risks.

SkyVision has reduced our perimeter security system's false alarm rate by over 60%. This not only saves significant manpower but, more importantly, allows us to focus more on real security threats, greatly improving both efficiency and confidence.

DaoAI Solutions and Products

DaoAI's core solution is based on the SkyVision 0-code video surveillance AI platform, combined with edge boxes for real-time analysis and alerting. During deployment, we first assess the client's existing surveillance cameras to ensure video stream quality meets AI analysis requirements. Then, SkyVision edge boxes are deployed in each critical perimeter area. These boxes contain high-performance computing units capable of performing AI inference directly on the video streams locally, achieving millisecond-level responses and ensuring 100% local data processing without leaving the premises, meeting the client's stringent data security requirements. During the model training phase, using SkyVision's unique 0-code training interface, security personnel without programming knowledge can train customized behavior recognition models within hours by simply dragging and selecting a few positive sample video clips. For model changes or updates, similarly, only a small number of new samples are needed for the system to quickly adapt. SkyVision's behavior/event recognition capabilities, combined with DaoAI World's semantic understanding, can precisely distinguish various complex behaviors. When intrusion or climbing behavior is detected, the edge box immediately triggers real-time alerts and seamlessly integrates into the client's existing security management platform via SDK/API/Docker, enabling various alert methods such as sound and light linkage, SMS notifications, and APP pushes, ensuring timely and comprehensive security responses. Additionally, SkyVision can be linked with other DaoAI product lines (e.g., DaoAI Robot Vision for security patrol robots) to build a more comprehensive smart security system.

Through the DaoAI SkyVision platform, the client achieved an intelligent upgrade of their perimeter security. Key business values include: a false alarm rate reduction of -63%, significantly decreasing the security personnel's workload for invalid reviews; an increase in real threat detection rate to 99.4%, greatly enhancing the overall security protection level; alarm response time shortened from minutes to seconds, gaining valuable time for security intervention; concurrently, due to the system's efficiency, the client was able to optimize security manpower allocation, dedicating more resources to high-value risk management and early warning tasks, effectively controlling operational costs, and significantly improving the continuity and safety of production operations.