关键词: Faster R-CNN, 小目标检测, 特征融合, 图像增强, 深度学习

Abstract: In order to solve the problem that the high-frequency features such as image detail texture are lost in the process of feature extraction based on the convolutional neural network model to result in poor detection of small object, a target detection algorithm based on multi-layer frequency domain feature fusion is proposed. The algorithm uses the Faster R-CNN algorithm as the basic framework, and uses high-frequency enhanced images and contrast-enhanced images as input samples of the algorithm to improve the detection image quality. For objects with a small area, the scale of anchor point in the RPN network is changed. The multi-scale convolution feature fusion method is used to integrate features from different feature layers to solve the problem that the feature information of small objects is lost in the deep feature map. The experimental results show that the algorithm has good performance on the DAGM 2007 data set and the mAP reaches 97.9%. The algorithm has significantly better mAP for small objects in the PASCAL VOC 2007 data set than the original Faster R-CNN. 

Key words: Faster R-CNN, small object detection, feature fusion, image enhancement, deep learning