Application of Image Recognition Algorithm in Positioning and Emergency Call Function of Smart Safety Helmets

In terms of ensuring the safety of construction workers and cyclists, traditional positioning methods often rely on GPS signals, but the accuracy of GPS positioning is limited indoors or in signal-blocked areas. At the same time, the traditional way of asking for help may delay the rescue time due to cumbersome operation, noisy environment or psychological pressure in emergency situations. To address these problems, this paper first integrates high-definition cameras, sensors and wireless communication modules on the smart safety helmet (SSH). The captured image is then preprocessed by noise reduction, enhancement, cropping, etc., and key features such as facial features are identified through CNN. The extracted features are then matched and analyzed, and compared with pre-stored map data, landmark databases, etc. At the same time, CNN is used to identify safety signs in the image to determine whether the wearer is in a dangerous environment. The research results found that the average error of CNN-based positioning technology is 1.32 meters, which is significantly lower than the 3.56 meters of traditional GPS technology. In terms of the response time of the rescue function, the average response time of CNN-based technology is 500.5 milliseconds, and the rescue success rate is also significant. improve. Therefore, this study concludes that CNN can improve the positioning accuracy and rescue response speed of SSHs, improve rescue efficiency and safety, and show great potential in the field of safety.