改进特征金字塔网络的Mask RCNN研究综述

A Survey of Mask RCNN Research with Improved Feature Pyramid Network

摘要: 随着计算机视觉的发展，目标检测技术的精度成为科研人员的重要研究内容之一。目标检测分为one-stage和two-stage两种检测方法，其中YOLO，SSD属于一阶段检测，R-CNN (Fast RCNN, Faster RCNN, Mask RCNN)属于两阶段检测。目标检测的精度依赖于特征提取的好坏，特征金字塔是用于检测不同尺度的对象的识别系统中的基本组件。Mask RCNN是基于分割掩码区域建议卷积神经网络的两阶段目标检测算法，精度相对较高，本文主要从它的特征金字塔网络出发，对近几年特征金字塔网络的改进算法进行研究。研究发现：基于原特征金字塔网络引入新的自底向上或自顶向下侧边连接的特征融合路径可以实现对底层信息的充分利用；通过双向、分层跳连等融合方法可以提高小目标检测的正确率。改进算法有效地提高了目标检测的精度。

Abstract: With the development of computer vision, the accuracy of target detection technology has become one of the important research contents of researchers. Target detection is divided into one-stage and two-stage detection methods. Among them, YOLO and SSD belong to one-stage detection, and R-CNN (Fast RCNN, Faster RCNN, Mask RCNN) belongs to two-stage detection. The accuracy of object detection depends on the quality of feature extraction, and feature pyramid is a basic component in a recognition system for detecting objects of different scales. Mask RCNN is a two-stage target detection algorithm based on the segmentation mask area proposal convolutional neural network, with relatively high accuracy. This paper mainly starts from its feature pyramid network, and studies the improved algorithm of feature pyramid network in recent years. The research found that: based on the original feature pyramid network, the introduction of a new bottom-up or top-down side-connected feature fusion path can fully utilize the underlying information; the fusion methods such as bidirectional and hierarchical jump connections can improve accuracy rate of small target detection. The improved algorithm effectively improves the accuracy of target detection.