Abstract: The application of intelligent air conditioning is inseparable from high-performance human target detection algorithm, so it is necessary to improve the detection accuracy and positioning accuracy of infrared human target. First, YOLOv5 is improved, and SIOU loss function and CA attention mechanism are introduced respectively to improve the detection ability of the model. Secondly, according to the scene requirements of household air conditioning and the characteristics of infrared images, a set of targeted data enhancement method is designed to further optimize the training effect of the model. Finally, the improved model is applied to the actual scene recognition of the air conditioning prototype. The results show that the improved YOLOv5 model is superior to the traditional methods in human target detection accuracy and positioning accuracy, and has certain practical value.

Key words: Infrared sensor, YOLOv5, Algorithm optimization, Data enhancement, Air conditioning