Object detection algorithm based on adaptive focal CRIoU loss

XIAO Zhen-jiu; ZHAO Hao-ze; ZHANG Li-li; XIA Yu; GUO Jie-long; YU Hui; LI Cheng-long; WANG Li-wen

doi:10.37188/CJLCD.2023-0005

您当前的位置：

首页 >

文章列表页 >

Object detection algorithm based on adaptive focal CRIoU loss

Object Detection and Recognition | 更新时间：2023-11-03

- Object detection algorithm based on adaptive focal CRIoU loss
- Chinese Journal of Liquid Crystals and Displays Vol. 38, Issue 11, Pages: 1468-1480(2023)
- 作者机构：
  
  1.辽宁工程技术大学软件学院，辽宁葫芦岛 125000
  2.中国兵器工业集团航空弹药研究院有限公司，黑龙江哈尔滨， 150000
  3.上海宇航系统工程研究所，上海 201100
  4.中国科学院海西研究院泉州装备制造研究中心，福建泉州 362000
- 作者简介：
- 基金信息：
  
  FuJian Science and Technology Plan(2021T3003);QuanZhou Science and Technology Plan(2021C065L)
- DOI：10.37188/CJLCD.2023-0005
  CLC：
扫描看全文
XIAO Zhen-jiu, ZHAO Hao-ze, ZHANG Li-li, et al. Object detection algorithm based on adaptive focal CRIoU loss. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1468-1480(2023)
DOI：

XIAO Zhen-jiu, ZHAO Hao-ze, ZHANG Li-li, et al. Object detection algorithm based on adaptive focal CRIoU loss. [J]. Chinese Journal of Liquid Crystals and Displays 38(11):1468-1480(2023) DOI： 10.37188/CJLCD.2023-0005.

摘要

在目标检测任务中，传统的边界框回归损失函数所回归的内容与评价标准IoU（Intersection over Union）之间存在不相关性，并且对于边界框的回归属性存在一定不合理性，使得回归属性不完整，降低了检测精度和收敛速度，甚至还会造成回归阻碍的情况。并且在回归任务中也存在样本不均衡的情况，大量的低质量样本影响了损失收敛。为了提高检测精度和回归收敛速度提出了一种新的边界框回归损失函数。首先确定设计思想并设计IoU系列损失函数的范式；其次在IoU损失的基础上引入两中心点形成矩形的周长和两框形成的最小闭包矩形周长的比值作为边界框中心点距离惩罚项，并且将改进的IoU损失应用到非极大值抑制（Non-Maximum Suppression，NMS）处理中。接着引入两框的宽高误差和最小外包框的宽高平方作为宽高惩罚项，确定CRIoU（Complete Relativity IoU，CRIoU）损失函数。最后在CRIoU的基础上加入自适应加权因子，对高质量样本的回归损失加权，定义了自适应聚焦CRIoU（Adaptive focal CRIoU，AF-CRIoU）。实验结果表明，使用AF-CRIoU损失函数对比传统非IoU系列损失的检测精度最高相对提升了8.52%，对比CIoU系列损失的检测精度最高相对提升了2.69%，使用A-CRIoU-NMS（Around CRIoU NMS）方法对比原NMS方法的检测精度提升0.14%。将AF-CRIoU损失应用到安全帽检测中，也达到了很好的检测效果。

Abstract

In the object detection task， there is no correlation between the regression content of the traditional bounding box regression loss function and the evaluation standard IoU（Intersection over Union）， and there is some irrationality for the regression attribute of the bounding box， which reduces the detection accuracy and convergence speed. In addition， the sample imbalance also exists in the regression task， and a large number of low-quality samples affect the loss function convergence. In this paper， a novel loss function， termed as CRIoU（Complete Relativity Intersection over Union），is proposed to improve the detection accuracy and convergence speed. Firstly， this work determines the design idea and determines the improved IoU loss function normal form. Secondly， on the basis of IoU loss， the ratio of the perimeter of the rectangle formed by the two center points and the minimum closure area formed by the two frames is introduced as the penalty term for the distance between the center points of the bounding box， and the improved IoU loss is applied to the non-maximum suppression.Then， the side error of the two frames and the side square of the minimum bounding box are introduced as the side penalty term，a novel loss function， termed as CRIoU（Complete Relativity Intersection over Union）， is proposed. Finally， on the basis of CRIoU， an adaptive weighting factor is added to weight the regression loss of high-quality samples， and an AF-CRIoU （Adaptive focal CRIoU） is defined. The experimental results show that the detection accuracy of the AF-CRIoU loss function compared with the traditional non IoU series loss is up to 8.52%， the detection accuracy of the CIoU series loss is up to 2.69%， and the A-CRIoU NMS （Around CRIoU Non Maximum Suppression） method compared with the original NMS method is up to 0.14%. In addition， AF-CRIoU loss is applied to the detection of safety helmet， which also achieves good detection results.

关键词

目标检测边界框回归IoU损失函数非极大值抑制自适应聚焦损失

Keywords

object detectionbounding box regressionIoU loss functionnon-maximum suppressionadaptive focal loss

references

张吉友，张荣芬，刘宇红，等. 基于注意力机制的多模态图像语义分割［J］. 液晶与显示，2023，38（7）：975-984． doi: 10.37188/cjlcd.2022-0309http://dx.doi.org/10.37188/cjlcd.2022-0309

ZHANG J Y， ZHANG R F， LIU Y H， et al. Multimodal image semantic segmentation based on attention mechanism ［J］. Chinese Journal of Liquid Crystals and Displays， 2023， 38（7）： 975-984.（in Chinese）. doi: 10.37188/cjlcd.2022-0309http://dx.doi.org/10.37188/cjlcd.2022-0309

张勇，郭杰龙，汪帆，等. 基于多级联递进卷积结构的图像去雨算法［J］. 液晶与显示，2023，38（10）：1409-1422. doi: 10.37188/CJLCD.2022-0383http://dx.doi.org/10.37188/CJLCD.2022-0383

ZHANG Y， GUO J L， WANG F， et al. Image rain removal algorithm based on multi-cascade progressive convolution structure ［J］. Chinese Journal of Liquid Crystals and Displays， 2023，38（10）： 1409-1422. （in Chinese）. doi: 10.37188/CJLCD.2022-0383http://dx.doi.org/10.37188/CJLCD.2022-0383

CHEN L， AI H Z， ZHUANG Z J， et al. Real-time multiple people tracking with deeply learned candidate selection and person Re-identification ［C］. IEEE International Conference on Multimedia and Expo. San Diego： IEEE， 2018： 1-6. doi: 10.1109/icme.2018.8486597http://dx.doi.org/10.1109/icme.2018.8486597

DONG J Y， ZHANG L Y， ZHANG H W， et al. Occlusion-aware GAN for face de-occlusion in the wild ［C］. 2020 IEEE International Conference on Multimedia and Expo （ICME）. London： IEEE， 2020： 1-6. doi: 10.1109/icme46284.2020.9102788http://dx.doi.org/10.1109/icme46284.2020.9102788

ZUO C， QIAN J M， FENG S J， et al. Deep learning in optical metrology： a review ［J］. Light： Science & Applications， 2022， 11（1）： 39. doi: 10.1038/s41377-022-00714-xhttp://dx.doi.org/10.1038/s41377-022-00714-x

SITU G. Deep holography ［J］. Light： Advanced Manufacturing， 2022， 3（2）： 278-300. doi: 10.37188/lam.2022.013http://dx.doi.org/10.37188/lam.2022.013

LUO Y， ZHAO Y F， LI J X， et al. Computational imaging without a computer： seeing through random diffusers at the speed of light ［J］. eLight， 2022， 2（1）： 4. doi: 10.1186/s43593-022-00012-4http://dx.doi.org/10.1186/s43593-022-00012-4

LECUN Y， BOTTOU L， BENGIO Y， et al. Gradient-based learning applied to document recognition ［J］. Proceedings of the IEEE， 1998， 86（11）： 2278-2324. doi: 10.1109/5.726791http://dx.doi.org/10.1109/5.726791

SHELHAMER E， LONG J， DARRELL T. Fully convolutional networks for semantic segmentation ［C］//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston： IEEE， 2015： 3431-3440. doi: 10.1109/cvpr.2015.7298965http://dx.doi.org/10.1109/cvpr.2015.7298965

KRIZHEVSKY A， SUTSKEVER I， HINTON G E. ImageNet classification with deep convolutional neural networks ［C］//Proceedings of the 25th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2012： 1097-1105.

YANG Z T， SUN Y N， SHU L， et al. 3DSSD： Point-based 3D single stage object detector ［C］//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle： IEEE， 2020： 11037-11045. doi: 10.1109/cvpr42600.2020.01105http://dx.doi.org/10.1109/cvpr42600.2020.01105

GIRSHICK R， DONAHUE J， DARRELL T， et al. Rich feature hierarchies for accurate object detection and semantic segmentation ［C］//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus： IEEE， 2014： 580-587. doi: 10.1109/cvpr.2014.81http://dx.doi.org/10.1109/cvpr.2014.81

HEARST M A， DUMAIS S T， OSUNA E， et al. Support vector machines ［J］. IEEE Intelligent Systems and their applications， 1998， 13（4）： 18-28. doi: 10.1109/5254.708428http://dx.doi.org/10.1109/5254.708428

GIRSHICK R. Fast R-CNN ［C］//Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago： IEEE， 2015： 1440-1448. doi: 10.1109/iccv.2015.169http://dx.doi.org/10.1109/iccv.2015.169

REN S Q， HE K M， GIRSHICK R， et al. Faster R-CNN： Towards real-time object detection with region proposal networks ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2017， 39（6）： 1137-1149. doi: 10.1109/tpami.2016.2577031http://dx.doi.org/10.1109/tpami.2016.2577031

REDMON J， DIVVALA S， GIRSHICK R， et al. You only look once： Unified， real-time object detection ［C］//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas： IEEE， 2016： 779-788. doi: 10.1109/cvpr.2016.91http://dx.doi.org/10.1109/cvpr.2016.91

REDMON J， FARHADI A. YOLO9000： better， faster， stronger ［C］//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolu： IEEE， 2017： 65117-6525. doi: 10.1109/cvpr.2017.690http://dx.doi.org/10.1109/cvpr.2017.690

REDMON J， FARHADI A. YOLOv3： An incremental improvement ［J/OL］. arXiv， 2018： 1804.02767. doi: 10.1109/cvpr.2017.690http://dx.doi.org/10.1109/cvpr.2017.690

BOCHKOVSKIY A， WANG C Y， LIAO H Y M. YOLOv4： Optimal speed and accuracy of object detection ［J/OL］. arXiv： 2004.10934， 2020.

GE Z， LIU S T， WANG F， et al. YOLOX： Exceeding YOLO series in 2021 ［J/OL］. arXiv， 2021： 2107.08430.

YU J H， JIANG Y N， WANG Z Y， et al. UnitBox： An advanced object detection network ［C］//Proceedings of the 24th ACM International Conference on Multimedia. Amsterdam： ACM， 2016： 516-520. doi: 10.1145/2964284.2967274http://dx.doi.org/10.1145/2964284.2967274

REZATOFIGHI H， TSOI N， GWAK J， et al. Generalized intersection over union： A metric and a loss for bounding box regression ［C］//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach： IEEE， 2019： 658-666. doi: 10.1109/cvpr.2019.00075http://dx.doi.org/10.1109/cvpr.2019.00075

ZHENG Z H， WANG P， LIU W， et al. Distance-IoU loss： Faster and better learning for bounding box regression ［C］//Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence. Washington： AAAI Press， 2020： 12993-13000. doi: 10.1609/aaai.v34i07.6999http://dx.doi.org/10.1609/aaai.v34i07.6999

SERMANET P， EIGEN D， ZHANG X， et al. Overfeat： Integrated recognition， localization and detection using convolutional networks ［C］. 2nd International Conference on Learning Representations. Banff： ICIR， 2014.

LIU W， ANGUELOV D， ERHAN D， et al. SSD： Single shot multiBox detector ［C］. 14th European Conference on Computer Vision. Amsterdam： Springer， 2016： 21-37. doi: 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2

LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection ［C］//Proceedings of the 2017 IEEE International Conference on Computer Vision. Venice： IEEE， 2017： 2999-3007. doi: 10.1109/iccv.2017.324http://dx.doi.org/10.1109/iccv.2017.324

TAN M X， CHEN B， PANG R M， et al. MnasNet： Platform-aware neural architecture search for mobile ［C］//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach： IEEE， 2019： 2815-2823. doi: 10.1109/cvpr.2019.00293http://dx.doi.org/10.1109/cvpr.2019.00293

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Object detection method based on CIoU improved bounding box loss function

Mask wearing detection based on improved YOLOv7

Lightweight and high-precision object detection algorithm based on YOLO framework

Related Author

No data

Related Institution

Fujian Institute of Research on the Structure of Matter， Chinese Academy of Sciences

Quanzhou Institute of Equipment Manufacturing Haixi Institutes， Chinese Academy of Sciences

College of Information Science and Engineering， Wuhan University of Science and Technology

Engineering Research Center of Metallurgical Automation and Measurement Technology， Ministry of Education

Shandong Key Laboratory of Industrial Control Technology

Address：No.3888 Dong Nanhu Road, Changchun, Jilin, China 130033 Postal code：130033
Tel：0431-86176059 Email：yjxs@ciomp.ac.cn
Technical support is provided by Beijing Founder electronics co., LTD 吉ICP备11002662号-17 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰