DFENet: Double Feature Enhanced Class Agnostic Counting Methods

Abstract

Object counting is a basic computer vision task, which can estimate the number of each object in an image, thus providing valuable information. In dense scenes, there are huge differences in target individual scale, and the different target individual scale leads to low accuracy of target count. In addition, most of the existing target count datasets in the field require a lot of manual creation and annotation, which increases the cost and difficulty of the dataset, lack of ease of use and portability. To solve these problems, this paper proposes a class agnostic counting method Double Feature Enhancement Net based on improved Bilinear Matching Network+ (BMNet+). By introducing the feature enhancement module based on the principle of conditional random field and the adaptively spatial feature fusion module, combined with the feature similarity measurement strategy of bilinear matching network, the method can effectively extract the target features of different scales, enhance the adaptability to the targets with large scale changes, and improve the counting performance of the network. Experiments were carried out on FSC-147 data set, and the experimental results show that the proposed model has been further improved in counting accuracy. The MAE and MSE of the verification set are 15.03 and 54.53 respectively. In the test set, MAE reaches 13.65, MSE reaches 89.54, and the counting performance is at the advanced level in the field.