Research on the Application of Hotel Cleanliness Compliance Detection Algorithm Based on WGAN

Aiming at the problems of irregular cleaning and supervision difficulties in the cleaning process of hotel bathrooms, a target detection algorithm based on deep learning is proposed to detect the cleaning process transmitted by the sensor in real time and analyze its prescriptivity. However, the cleaning process has factors such as occlusion, light influence and insufficient data volume, resulting in inefficient detection. Therefore, this paper proposes a deep convolutional generation adversarial network (DCGAN) as the basic framework to expand the data set, improve the adaptability and robustness of the detector to different detection targets, take advantage of the fast speed and high accuracy of the YOLOv5 target detection network to detect the target, and then design a compliance detection network algorithm to detect whether the target meets the cleanliness standards. Experimental results show that the method has rapidity, practicality and high accuracy, and fully meets the engineering needs of hotel cleaning process detection and supervision.