Convolutional neural network is widely used in the field of object detection. In this paper, a novel anchor-free two-stage object detection algorithm is investigated. Region proposals are produced via corner points extracted based on CornerNet. In order to improve the inception ability to the internal information of the object, central pooling is introduced in the algorithm to enhance the features of interal regions for internal feature point detection. A large number of false-positive proposals can be filtered out by checking whether the internal key points exist in the internal area. The remaining proposals are fed into a multivariate classifier to obtain the final result. The proposed algorithm has been tested on the data set of