Aiming at the problem of the extremely low visibility of water scene low at night, which results in the difficulty in detecting and locating personnel targets, the author combines infrared thermal imaging technology with deep learning object detection algorithm to study an object detection method for people in dark water area. After multi-scene field collection, a set of human target data set IR-YZ in thermal imaging water scene was independently constructed. On the basis of the performance of the IR-YZ data set and compared with the classical object detection methods, environmental characteristics, an enhanced lightweight water object detection network infrared water person target-YOLO is proposed, featuring the characteristics of thermal imaging and water areas. The experimental results show that the IWPT-YOLO algorithm has the advantages of being more accurate, faster and more concise than those of the classical algorithm. The model size is 93 MB, the average precision mAP reaches 85. 34%, and the detection speed reaches 20. 975 FPS. Compared with the classic algorithm YOLOv3 network and SSD network, the model size, average precision and detection speed are all improved. It verifies that the IWPT-YOLO algorithm has better detection performance and more obvious advantages for the characteristics of thermal imaging and water areas.