In order to meet the needs of spraying robots for spraying quality detection, the improved padim modeling and ResNet network are fused by using transfer learning to build an integrated model of spray quality detection for the autonomous spray robot. The model can be used for defect location and classification at the same time by extracting image features once. At the defect location end, the Padim model is improved to reduce the network computing consumption caused by feature redundancy, and the patch embedding vector semantic layer obtained by the ResNet-18 network is first changed from the original three layers to a single second layer. Then the dimension of feature expression is reduced from 100 to 20 dimensions. Finally, the normal distribution model obtained by training the positive samples and the test image are used for defect location. At the defect classification end, the pre-trained ResNet-18 network is re-trained with negative samples, and the ResNet-18 classification model is obtained to classify the test images for defects. After experiments, the integrated model is transplanted into the Jetson nano mobile terminal. The parameter quantity is 11. 69 M, the positioning accuracy is 94. 5%, and the classification accuracy is as high as 99. 6%. The detection time is 0. 730 s when the robot displacement speed is 0. 02 m/ s, and there will be no missing frame detection, which meets the requirements of real-time detection.