At present, normalized body temperature monitoring is implemented in indoor public places. The existing temperature measurement solutions have disadvantages such as slow temperature measurement speed, low temperature measurement accuracy, and small monitoring range. In view of the existing problems, this paper proposes an improved target detection algorithm based on YOLOv5, which is used with binocular cameras to monitor pedestrian body temperature in real time. The algorithm introduced DenseFuse to fuse the input visible light and infrared images at the feature level to obtain feature information of different meanings and enhance the feature structure. The Decoupled Head was used to replace the original coupled detection head to enhance the expression ability of the output and improve the detection accuracy. The experimental results show that compared with the original YOLOv5, the recall rate of the proposed method in this paper is increased by 6.29 percentage points, and the average accuracy rate is increased by 6.37 percentage points, which can meet the needs of efficient and accurate real-time detection in large passenger flow scenarios.