Based on the development demand of intelligent water area monitoring system, a personnel recognition algorithm for monitored water area is proposed. After data collection of the water area scene, data cleaning and labeling, a personnel category dataset YZ-Water4 under the monitored water area scene was independently constructed, with a total of 8 092 images and 24 011 tags. Based on the performance of the object detection algorithm YOLO v7 and the characteristics of the water area scene, object detection algorithm YOLOWA (you only look once-water area) for water environment is proposed. First, the FReLU activation function which is proposed for visual tasks is used to replace the activation function in YOLO v7 algorithm. Secondly, the attention mechanism is integrated into algorithm to improve the feature extraction ability of the algorithm. Finally, SIOU loss function is chosen to replace CIOU loss function in YOLO v7 algorithm to optimize the training process. The experimental results show that compared with the original algorithm, YOLO-WA has increased the precision rate from 82. 3% to 86. 9%, recall rate from 92. 0% to 92. 8%, mean average precision from 88. 4% to 90. 6%, and the processing speed is 85 frame per second, meeting the accuracy and speed requirements of real-time run.