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NPP reactor containment dust can easily turn into radioactive dust, endangering staff health and the environment. However, the nuclear reactor containment wall-climbing cleaning robot cleans blindly without the ability to clean the dust in a timely and thoroughly. In this paper, ShuffleNetV2-YOLOv5s (S-YOLOv5s) model is proposed to solve the problem of wall-climbing robots unable to detect different categories of dust in time. The use of ShuffleNetV2 in the backbone of the network not only ensures a large number of characterized channels and a large network capacity, but also reduces the complexity of the model; SIoU is chosen for the loss function to improve the model accuracy. Then, planar cleaning index (PCI) is proposed by combining the results of S-YOLOv5s to evaluate whether the wall-climbing cleaning robot cleans thoroughly. Compared to other methods, PCI considers amount and area occupation of different classes of dust. The dust data set is collected to train the designed model, and the model size is reduced to 14 % of the original model, and the FPS is 7.313 higher than the original model. Especially when compared with the state-of-the-art lightweight methods, our model has smaller model size and higher recognition speed. Experimental results have shown that our dust detection and cleanliness assessment method can be used on a wall-climbing cleaning robot to clean dust in time and thoroughly.