Concise feature pyramid region proposal network for multi-scale object detection

Object detection is a hot research issue in the field of computer vision. Many methods focus on detecting large objects. And features of small objects are easily weakened or even disappeared after multiple convolution layers. So the detection rate of multi-scale objects is unsatisfied. Aiming at this problem, a concise feature pyramid region proposal network (CFPRPN) is proposed to address the problem of small objects detection in this paper without missing the large objects. In the process of object detection, we propose a new method of adjustment for the object location. So the balanced detection of multi-scale objects is realized. CFPRPN combines image pyramids and feature pyramids. An image pyramid consists of scaled versions of an image and the feature pyramids produce multiple layers’ feature maps. They are both conducive to capturing the feature information of small objects in deep convolutional networks. At the same time, proposals of overlapping sizes from different layers are applied to improve the recall rate of multi-scale objects. These series operations are beneficial for CFPRPN to extract better proposals. We experimentally prove that after adding the fine-tuning location, the detection rate of multi-scale object is further improved. The inspiring thing is that refining location method is suitable for most algorithms of object detection.