An improved approach for two-stage detection model

With the rapid development of deep learning models, the performance of object detection have made great success in recent years. However, the problem of low detection efficiency still exists in two-stage detection model. In this paper, we design a lightweight fully convolution neural network(LFCNN) as backbone to extract features more efficiently. Firstly, LFCNN is a lightweight network with only a small number of network parameters, which ensures that it can complete the feature extraction task more quickly while maintaining detection accuracy. Secondly, LFCNN uses residual connection to ensure the performance of the deep network and uses dense connection to realize the reuse and fusion of multi-layer features of the network, which significantly improve the detection accuracy. Moreover, we also come up with a novel method called anchor scale generator(ASG) to obtain the proper predefined anchor scales for generating more accurate region proposals, which further enhances localization ability of objects. A large number of experiments on Pascal VOC and COCO datasets show that our approach is superior to other methods in both bounding boxes localization accuracy and detection performance.