Study on application of fine-tuning convolutional neural network in crack detection of building facade
The application of deep learning to the building facade crack detection has the advantage of high efficiency and superior objectivity.However,the diversity of building facade makes it time-consuming to simply use a massive sample set to train the model and difficult to reach a satisfying recognition.In order to improve the precision,recall and training efficiency,a fine-tuning training method based on transfer learning was proposed.After using a large sample set from other buildings for pre-training,fine-tuning training with a small amount of sample from the building under detection was conducted,and then the fine-tuned model was used to detect other parts of the building.Through the comparison of the test results and training time-consumption,the superiority of applying fine-tuning convolutional neural network to building facade crack detection was proved,the influence of frozen layers on fine-tuning model was studied.
deep learningfine-tuned convolutional neural networkbuilding facadecrack detectiontraining sample
万方数据
维普
