Application of Improved Cascade R-CNN Algorithm in Target Detection

ZHANG Na; BAO Zi-qun; LUO Yuan; WU Biao; TU Xiao-mei

doi:10.12263/DZXB.20220116

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Application of Improved Cascade R-CNN Algorithm in Target Detection

PAPERS | 更新时间：2025-12-08

- Application of Improved Cascade R-CNN Algorithm in Target Detection
- ACTA ELECTRONICA SINICA Vol. 51, Issue 4, Pages: 896-906(2023)
- 作者机构：
  
  1.浙江理工大学计算机科学与技术学院(人工智能学院),浙江杭州 310018
  2.浙江理工大学理学院,浙江杭州 310018
  3.浙江广厦建设职业技术大学,浙江东阳 322100
- 作者简介：
- 基金信息：
  
  Key R&D Project of Zhejiang Province(2020C03094);National College Students Innovation and Entrepreneurship Training Program(202010338024);Education General Scientific Research Project of Zhejiang Province(Y202147659)
- DOI：10.12263/DZXB.20220116
  CLC： TP391;
- Received：25 January 2022，
  
  Revised：2022-07-19，
  
  Published：25 April 2023
- 稿件说明：
移动端阅览
张娜,包梓群,罗源等.改进的Cascade R-CNN算法在目标检测上的应用[J].电子学报,2023,51(04):896-906.

ZHANG Na,BAO Zi-qun,LUO Yuan,et al.Application of Improved Cascade R-CNN Algorithm in Target Detection[J].ACTA ELECTRONICA SINICA,2023,51(04):896-906.
张娜,包梓群,罗源等.改进的Cascade R-CNN算法在目标检测上的应用[J].电子学报,2023,51(04):896-906. DOI： 10.12263/DZXB.20220116.

ZHANG Na,BAO Zi-qun,LUO Yuan,et al.Application of Improved Cascade R-CNN Algorithm in Target Detection[J].ACTA ELECTRONICA SINICA,2023,51(04):896-906. DOI： 10.12263/DZXB.20220116.

摘要

针对Cascade R-CNN目标检测算法中存在检测精度较低以及目标遮挡问题，本文提出一种改进的Cascade R-CNN网络目标检测算法.该算法在主干网络ResNet101中引入可切换空洞卷积模块（Switchable Atrous Convolution，SAC），该模块主要由两个全局上下文模块以及SAC组件构成，采用SAC组件以不同的空洞卷积率对特征进行卷积，并使用Switch函数收集特征来提高特征提取能力.同时，在ResNet101残差网络中引入坐标注意力机制（Coordinate Attention，CA），该机制将位置信息嵌入通道注意力中，用于更好地获取方向感知和位置感知信息，进而提高目标检测精度.此外，针对目标遮挡问题，引入Repulsion Loss损失函数.该损失函数主要由吸引项和排斥项组成，吸引项使得预测框和匹配上的目标框尽可能接近，排斥项使得预测框远离错误目标，进而减少非极大值抑制（Non-Maximum Suppression，NMS）的误检，提高目标检测中遮挡问题的检测精度.实验结果表明，在公开的科大讯飞AI挑战赛数据集上，与原算法测试性能相比，改进的Cascade R-CNN网络对该数据集检出率增长了2.39%，改进算法的识别精度有一定的提高.

Abstract

An improved target detection algorithm based on Cascade R-CNN network is proposed to solve the problems of low detection accuracy and target occlusion in the target detection algorithm based on Cascade R-CNN. The algorithm introduces a switchable atrous convolution (SAC) module into the backbone ResNet101

which is composed of two global context modules and SAC components. The SAC component is used to convolution the features with different void convolution rates

and the Switch function is used to collect the features to improve the ability of feature extraction. At the same time

coordinate attention (CA) is introduced in ResNet101 residual network

which embeds position information into channel attention

and is used to obtain direction and position information better to improve the accuracy of target detection. In addition

aiming at the problem of target occlusion

this paper introduces the repulsion loss function

which is mainly composed of the attraction term and the exclusion term. The attraction term makes the prediction box and the target box on the matching as close as possible

and the exclusion term makes the prediction box away from the wrong target

thereby reducing the false detection of non-maximum suppression (NMS) and improving the detection accuracy of the occlusion problem in object detection. The experimental results show that the detection rate of the improved Cascade R-CNN network is 2.39% higher than that of the original algorithm on the open IFLYTEK Challenge dataset

the recognition accuracy of the improved algorithm is improved to a certain extent.

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