Research on small sample defect detection based on optimized Faster R-CNN algorithm
With the development of automatic detection technology,defect detection technology based on deep learning is gradually becoming a research hotspot in industrial and academic fields due to its high precision,high efficiency and non-contact characteristics.In order to solve the problems of model overfitting and low detection accuracy caused by insufficient samples and unbalanced categories of product defect data sets in actual industrial production,a defect detection model Faster R-CNN-H-BFC based on Faster R-CNN algorithm framework optimization is proposed.The illusion network based on multi-layer perceptron(MLP)can learn class sharing features from base classes with rich samples and generate additional illusion samples for new classes for model training.Aiming at the problems of low recognition accuracy and poor detection effect of Faster R-CNN,the original VGG16 backbone network is replaced with ResNet50 with residual structure,and Feature Pyramid Networks(FPN)is introduced to realize multi-scale feature fusion,and the Convolutional Block Attention Module(CBAM)is added to enhance the feature extraction ability of the model.Experiments and data show that the improved defect detection model has better detection effect in very few sample scenarios,and the average detection accu-racy is 3.11%higher than that before improvement.