The application potential of artificial intelligence technology in the field of infrared thermal imaging

Currently, artificial intelligence technology is accelerating the integration and application of infrared thermal imaging technology, and its application in the field of infrared thermal imaging is rapidly promoted. AI technology can empower infrared thermal imaging technology at multiple levels such as image processing algorithms and target detection and recognition.

In terms of image processing algorithms, AI technology can be used for infrared image enhancement, infrared tone mapping enhancement, infrared image super-resolution reconstruction, etc. In terms of infrared image enhancement, deep learning models are used to learn key features in images and are enhanced based on these features to improve image contrast, details and clarity, thereby making infrared images easier to analyze and understand. Based on the classic AI deep denoising networks such as DnCnn and REDNet, the infrared image denoising effect is superior to traditional denoising algorithms. Tone mapping maps the brightness values of infrared images to a range suitable for human eye observation. At present, deep AI models such as DeepTone, HDRNet, and RetinexNet have been used to enhance the tone mapping of infrared images. The effect is better than traditional mapping algorithms and can significantly improve Contrast and visualization of images to highlight key information and details. The deep model based on AI deep learning can perform super-resolution reconstruction of infrared images very well, infer higher-resolution images from low-resolution images, and enhance the clarity and accuracy of the image. SRGAN (Super-Resolution Generative Adversarial Network) is a super-resolution reconstruction model based on a generative adversarial network. It achieves mapping from low-resolution images to high-resolution images through the competition process between training generators and discriminators. ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) is an improved model of SRGAN, which introduces perceptual loss and residual block structure to provide better super-resolution reconstruction effect. RCAN (Residual Channel Attention Networks) is a lightweight super-resolution model that uses the channel attention mechanism to extract image features and transfer information through residual connections to achieve high-quality super-resolution reconstruction.

Target detection and recognition have extensive needs in military, security, and unmanned driving. The core of target recognition in infrared video is to first detect specific targets such as pedestrians from images, and then classify and identify the target's actions. By training some classic deep target detection models, such as Faster R-CNN, YOLO, and SSD models, AI empowerment can achieve accurate positioning and identification of different targets (such as human bodies, vehicles, etc.) in infrared images, providing More reliable target monitoring capabilities, accurate identification and tracking of human targets in infrared images, can be used in video surveillance, border security and other fields. In driverless applications, AI vision technology is used to detect and identify other road vehicles from infrared images, thereby providing judgment conditions and possible situations for driving decisions. Infrared and visible light image fusion is an important branch in the field of image fusion. Based on multi-scale transformation expansion methods, generative adversarial network-based methods and auto-encoder-based deep learning fusion algorithms, the fused image has rich features of visible light images. It has background and detail information, good target information of infrared images, and the fusion image has good robustness.

With the long-term sustainable development of our country's economy and technology, our country's infrared thermal imaging technology has made great progress and continued to improve its professional and technical level, especially with the traction and support of major national projects, promoting uncooled infrared focal plane and intelligent processing The localization of "core" levels such as chips has become the development direction of thermal imaging technology. Currently, the uncooled infrared focal plane detector (VA-6100), empowered by AI technology, continues to improve the performance and intelligence level of infrared thermal imaging systems, reduce costs through mass production and application, and give full play to its monitoring and analysis functions. , accelerating its expansion and in-depth application in digital economy, autonomous driving, robots and other fields.

• Previous: Market application of uncooled infrared focal plane detectors
• Next: Choice between thermal imaging and night vision