SkyVision Platform: Achieving Early and Precise Identification of Smoke and Open Fire

With the wide application of AI technology in the field of people's livelihood, the demand for behavior recognition technology in the field of smart security is increasing day by day. The SkyVision platform of WeLinkirt has brought new breakthroughs in the early identification of smoke and open fire with advanced technology.

In the current trend of AI enabling people's livelihood, the field of smart security is in a stage of continuous exploration and innovation. For many large industrial parks, there are many potential safety hazards during daily production and operation, among which fire hazards are particularly prominent. There are various types of factories, warehouses and other places in the park, which are all key monitoring areas. Once the early signs of smoke and open fire appear, if they cannot be detected and handled in time, the fire may spread rapidly, causing not only serious property losses but also casualties, which will have a negative impact on the normal operation of the enterprise and social stability. Therefore, the early and precise identification of smoke and open fire has become a key link in the park's emergency security work.

Pain Points: Why is it Difficult?

Traditional smoke and open fire detection methods have multi-dimensional quantitative dilemmas. In terms of the missed detection rate, the missed detection rate of traditional methods is about 3%, which means that a considerable number of early signs of smoke and open fire cannot be detected in time. For example, in some areas with dim light or complex environments, traditional detection equipment may not be able to accurately capture weak smoke signals, thus increasing the risk of fire. From the perspective of the false alarm rate, the false alarm rate of traditional methods is as high as 20%. In the park, some normal production activities, such as sparks generated by welding and steam emissions, may be misjudged as smoke or open fire, resulting in frequent alarms. This not only wastes a lot of manpower to verify the situation, but also may cause the staff to be numb to the alarms, reducing the efficiency of emergency response. In addition, traditional detection methods are relatively weak in data processing and analysis capabilities, and it is difficult to accurately extract and judge the characteristics of smoke and open fire in complex scenarios. The root cause is that traditional detection technologies often rely on simple sensors and fixed threshold judgments, and cannot adapt to diverse environments and complex smoke and open fire forms, and have poor resistance to environmental interference.

Moreover, the update and maintenance costs of traditional detection equipment are relatively high, and enterprises need to invest a large amount of funds and manpower to ensure the normal operation of the equipment. At the same time, traditional detection methods lack effective management and utilization of data, and cannot provide comprehensive safety analysis and decision-making support for enterprises, which is difficult to meet the innovative development needs of behavior recognition technology in smart security.

Technical Principles

The SkyVision 0-code video surveillance AI platform of WeLinkirt adopts advanced computer vision algorithms and deep learning technologies. Computer vision algorithms can comprehensively extract and analyze the features of surveillance video images, and accurately judge whether there is smoke or open fire by identifying the shape, color and other features of smoke and open fire. For example, smoke usually presents an irregular shape and a fuzzy boundary, and the color is mostly gray or white; open fire has a bright color and an obvious flame contour. Deep learning technology continuously optimizes the parameters of the model through learning and training a large amount of smoke and open fire image data, improving the accuracy and robustness of identification. Even in complex environments, such as changes in light and different smoke concentrations, it can accurately identify the target.

The hardware foundation of the platform is a high-performance edge box, which can process surveillance video data in real-time. The edge box has powerful computing capabilities and can analyze and process video data locally, reducing the delay of data transmission to the cloud and realizing rapid alarm. Once the early signs of smoke or open fire are detected, the edge box can immediately issue an alarm to notify relevant personnel to take measures. At the same time, the SkyVision platform uses the semantic understanding ability of the DaoAI World model, which can accurately extract the semantic information of smoke and open fire from complex scenarios, avoiding misjudgments caused by environmental interference. Compared with traditional methods, which often only make judgments based on simple physical features, the SkyVision platform can deeply analyze the context information of the scenario through semantic understanding, accurately distinguish real smoke and open fire from other interfering factors, and greatly improve the accuracy of identification.

Typical Application Scenarios

• Chemical production workshops: In the chemical production process, various flammable and explosive gases and smoke may be generated. The difficulty in detection lies in distinguishing the normal gas emissions during the production process from the smoke in the early stage of a fire. The SkyVision platform analyzes the color, concentration, flow direction and other characteristics of the smoke, and combined with the judgment of the deep learning model, can accurately identify the early signs of a fire-related smoke.
• Timber processing warehouses: There is a large amount of timber piled up in the warehouse. Once a fire occurs, the fire spreads rapidly. The difficulty in detection is that the light in the warehouse is dim, and the texture of the timber surface may interfere with the identification of smoke. The platform uses the image enhancement function of the computer vision algorithm to improve the clarity of the image, and at the same time analyzes the dynamic characteristics of the smoke to accurately detect the presence of smoke.
• Electronic equipment assembly workshops: There are a large number of electrical equipment in the workshop, which may cause open fire due to short-circuits and other reasons. The difficulty in detection is that the open fire may be relatively weak, and there are various lighting interferences in the surrounding environment. The SkyVision platform can accurately identify the early signs of open fire by precisely identifying the color, brightness, flashing frequency and other characteristics of the flame.
• Food processing workshops: Steam and fumes may be generated in the workshop, which are easily confused with smoke. The difficulty in detection is to accurately distinguish these normal production phenomena from the smoke in the early stage of a fire. The platform uses the semantic understanding ability of the DaoAI World model and combines the context information of the scenario to accurately judge whether there is a fire hazard.

Implementation Case

A large industrial park covers a vast area, has multiple factories and warehouses, and a large number of employees. The park faces serious fire hazards in daily production, and the traditional smoke and open fire detection methods can no longer meet its safety needs. Before introducing the SkyVision platform of WeLinkirt, the detection rate of smoke and open fire in the park was about 97%, the false alarm rate reached 20%, and the missed detection rate was 3%. Due to frequent false alarms, the park needed to invest a large amount of manpower for verification, and fire accidents caused by missed detections also occurred from time to time, bringing great economic losses and downtime to the park.

During the implementation process, the technical team of WeLinkirt first conducted a detailed investigation and analysis of the actual situation of the park. According to the layout and production process of the park, they determined the distribution of monitoring points. Then, using the function of the SkyVision platform to support on - site hourly training of self-owned models, they quickly trained a suitable identification model for the park. During the model training process, the technical team continuously optimized the parameters to improve the accuracy of the model. After a period of debugging and optimization, the platform was officially launched and put into operation.

After the launch, the detection effect of smoke and open fire in the park has been significantly improved.

WeLinkirt's Solutions and Products

Centered on the SkyVision platform, it has many outstanding advantages. First of all, the platform supports on - site hourly training of self-owned models. Enterprises can quickly train suitable identification models according to the actual situation of their own parks. Whether it is different production processes, environmental conditions or monitoring needs, they can be met through flexible model training. In terms of behavior/event recognition, the platform can accurately identify the early signs of smoke and open fire, not missing any potential fire hazards. The real-time alarm function of the edge box is a highlight of the platform. Once an abnormal situation is detected, it can immediately issue an alarm to ensure that relevant personnel can take measures in time. Moreover, the platform ensures that 100% of local data does not leave the park, and adopts a safe data storage and processing mechanism to avoid the risk of data leakage, providing reliable data security for enterprises. At the same time, combined with the semantic understanding ability of the DaoAI World model, the accuracy of identification is further improved, and it can better adapt to complex and changeable scenarios.

Quantitative Results

The application of the SkyVision platform of WeLinkirt has achieved significant quantitative results. First of all, the detection rate has increased from the original 97% to 99.4%, greatly improving the ability to detect the early signs of smoke and open fire. This means that more fire hazards can be detected in time, providing stronger guarantee for the enterprise's safe production. Secondly, the false alarm rate has decreased by -70%, from the original 20% to 6%, reducing a large amount of unnecessary manpower investment. The staff no longer need to verify false alarm information frequently and can devote more energy to actual safety management work. In addition, due to the timely detection of early hazards, the downtime of the park caused by fires has been significantly reduced, saving huge economic losses for the enterprise. At the same time, the missed detection rate has decreased from the original 3% to <0.6%, further reducing the risk of fire and improving the overall safety level of the park.

This article was generated by AI. Customer cases are simulated scenarios based on real product capabilities and figures are illustrative; see product pages for official benchmarks.