Few-shot Object Detection Model based on Transfer Learning and Convolutional Neural Network

Abstract

Aiming at the problem that Faster R-CNN fails to meet the current situation of few-shot object detection, an improved few-shot object detection model is proposed based on transfer learning and Faster R-CNN. Transfer learning algorithm is applied by transferring trained weights which from similar source dataset initially. The attention mechanism is added to the feature extraction module of the Faster R-CNN algorithm model and the idea of feature pyramid is introduced to fuse feature maps to build an effective feature extraction model. At the same time, a regularization term of background suppression is added to loss function of Faster R-CNN to improve the accuracy of object detection. The experimental results show that not only the feature extraction ability of the object detection network is strengthened, but also the improved loss function can effectively suppress the background interference and make the model training more adequate as a result. Experimental results show that the proposed network can converge faster and the detection accuracy is improved compared with other methods.