Abstract: This paper addresses the current low level of intelligence in firefighting helmets, which fail to provide visual assistance to firefighters. It combines Augmented Reality (AR) with infrared thermal imaging technology to design an intelligent firefighting helmet. Based on the consideration that thermal infrared images often suffer from blurring, the paper explores an optimization of the Scharr edge detection algorithm to enhance edge extraction of thermal infrared images, which is then presented to firefighters via the AR display. The specific algorithm process includes: using Gaussian filtering to smooth the image and reduce the impact of noise on subsequent edge detection; adding filtering in six directions (0°, 45°, 90°, 135°, 225°, and 315°) on the traditional Scharr operator, generating multiple edge images; performing maximum value synthesis and normalization to ensure consistency and clarity of the edge information. Experimental results show that the proposed algorithm meets the real-time and practical application requirements. Compared to the traditional Scharr algorithm, the proposed algorithm improves the Structural Similarity Index (SSIM) by 15%, the Feature Similarity Index (FSIM) by 7.6%, and reduces the Mean Squared Error (MSE) by 3.3%.