Safety helmet recognition - construction safety "regulator"

The helmet recognition system independently developed by Fuwei Image, based on artificial intelligence image recognition technology, monitors the wearing of workers' helmets in real time, replaces manual supervision, and becomes the "supervisor" of safety production at the construction site.

Hard hat identification case diagram

I. Working principle

Hard hat recognition firstly utilizes the arrangement of on-site cameras to incorporate all complex and large-scale construction work scenes into the scope of the camera. At the same time, it can also capture all workers into images for analysis, which solves the old problem of omissions in manual supervision..Secondly, the video stream is transmitted to the helmet recognition system. The system analyzes the video in real time according to the high-efficiency algorithm independently developed by the company, analyzes the helmet wearing of all workers in a very short time, and marks the workers who do not wear helmets in the image.Finally, the illegal images or videos are saved locally for later verification and statistics.At the same time, real-time alarms are issued for violations, and HTTP push, WeChat messages, and on-site speaker prompts can be carried out as needed to remind on-site workers and supervisors to make corrections in time.

How helmet recognition works

Second, the important role of safety helmet identification

Hard hats can protect against injuries caused by falling objects or bumps, and are a powerful guarantee for the safety of workers.However, there are still people who often have a fluke mentality, lack of self-management awareness, do not wear hard hats, and insufficient on-site supervision, resulting in the occurrence of safety accidents.Once these accidents happen, they often cause huge loss of life and property.When it is difficult to achieve perfection by manual labor, it needs to use the power of technology to complete it. The Fuwei helmet recognition system can comprehensively supervise whether workers wear helmets, effectively ensuring the safe production of construction operations!

Hard hat identification case diagram

Three, a wide range of application scenarios

The application scenarios of the helmet identification system are very wide today.According to relevant laws and regulations, workers in construction sites, coal mines, electric power, factories and other construction sites must wear safety helmets to protect the safety of life and property. Both workers and enterprises must pay attention to it. Only by ensuring safety issues can the interests of enterprises and individuals be protected.Helmet recognition can be used to supervise the wearing of helmets in different scenarios through sample training and algorithm optimization.The algorithm model independently developed by Fuwei Image can be trained with small samples, adapt to different operating scenarios in a shorter time, and the accuracy rate is as high as 90%.

Workers wear safety helmets for construction

Four, multi-algorithm concurrency

Not only the hidden danger of not wearing a safety helmet during construction work, but also the safety of workers' lives and property due to fire, falls, and intrusion into the pro-area.How to solve these problems together is one of the difficult problems for all enterprises engaged in image recognition technology.After long-term and unremitting research and development, the safety management system developed by Fuwei Image supports multi-algorithm concurrency. According to the needs of the scene, the helmet recognition function can be freely matched with other systems to solve the existing safety hazards in an all-round way.Enterprise brings good news!

AI intelligence

Hard hat identification integrates the most advanced technology into safety management and control, and solves the most difficult safety problems at the lowest cost.Fuwei image helmet recognition is still developing algorithms and updating functions, just to better play the role of "supervisor" of safety production.

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