基于改进YOLOv4的水下垃圾清理机器人视觉检测算法

田满军; 李霞丽; 孔诗涵; 吴立成; 喻俊志

doi:10.1631/FITEE.2100473

Your Location：

Home >

Browse articles >

基于改进YOLOv4的水下垃圾清理机器人视觉检测算法

常规文章 | Updated：2022-09-08

- 基于改进YOLOv4的水下垃圾清理机器人视觉检测算法
- A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot^&
- 信息与电子工程前沿（英文） 2022年23卷第8期页码：1217-1228
- Affiliations：
  
  1.First Research Institute of the Ministry of Public Security of PRC, Beijing 100048, China
  2.School of Information Engineering, Minzu University of China, Beijing 100081, China
  3.Department of Advanced Manufacturing and Robotics, College of Engineering, Peking University, Beijing 100871, China
  4.State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Author bio：
  
  E-mail: tianmanjun2018@163.com
  xiaer_li@163.com
  kongshihan@pku.edu.cn
  wulicheng@tsinghua.edu.cn
  ‡Corresponding author
- Funds：
  
  National Natural Science Foundation of China(61725305;U1909206;T2121002;62073196);Postdoctoral Innovative Talent Support Program(BX2021010);S&T Program of Hebei Province, China(F2020203037)
- DOI：10.1631/FITEE.2100473
  中图分类号： TP242
- 收稿：2021-10-01，
  
  录用：2022-03-07，
  
  纸质出版：2022-08-23
- Accepted：
Scan QR Code
田满军, 李霞丽, 孔诗涵, 等. 基于改进YOLOv4的水下垃圾清理机器人视觉检测算法[J]. 信息与电子工程前沿（英文）, 2022,23(8):1217-1228.

Manjun TIAN, Xiali LI, Shihan KONG, et al. A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot^&[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1217-1228.
田满军, 李霞丽, 孔诗涵, 等. 基于改进YOLOv4的水下垃圾清理机器人视觉检测算法[J]. 信息与电子工程前沿（英文）, 2022,23(8):1217-1228. DOI： 10.1631/FITEE.2100473.

Manjun TIAN, Xiali LI, Shihan KONG, et al. A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot^&[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(8): 1217-1228. DOI： 10.1631/FITEE.2100473.

摘要

为解决水环境污染问题，依托基于视觉的水下垃圾自主清理机器人，提出一种基于改进YOLOv4的垃圾检测方法，可实现高速、高精度的目标检测。具体而言，选择YOLOv4算法作为执行目标检测的基本神经网络框架。为进一步提高检测精度，将传统YOLOv4改进为四尺度检测算法；为提高检测速度，对新模型进行模型剪枝操作。同时，将所提方法应用于水下机器人，实现了自主垃圾收集作业。检测速度可达66.67帧/秒，平均准确率可达95.099%；实验结果表明，改进后的YOLOv4算法在检测速度和精度方面均表现优秀。

Abstract

To tackle the problem of aquatic environment pollution

a vision-based autonomous underwater garbage cleaning robot has been developed in our laboratory. We propose a garbage detection method based on a modified YOLOv4

allowing high-speed and high-precision object detection. Specifically

the YOLOv4 algorithm is chosen as a basic neural network framework to perform object detection. With the purpose of further improvement on the detection accuracy

YOLOv4 is transformed into a four-scale detection method. To improve the detection speed

model pruning is applied to the new model. By virtue of the improved detection methods

the robot can collect garbage autonomously. The detection speed is up to 66.67 frames/s with a mean average precision (mAP) of 95.099%

and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.

关键词

Keywords

references

Albitar H , Dandan K , Ananiev A , et al. , 2016 . Underwater robotics: surface cleaning technics, adhesion and locomotion systems . Int J Adv Robot Syst , 13 ( 1 ): 7 . doi: 10.5772/62060 http://doi.org/10.5772/62060

Astapov S , Preden JS , Ehala J , et al. , 2014 . Object detection for military surveillance using distributed multimodal smart sensors . Proc 19th Int Conf on Digital Signal Processing , p. 366 - 371 . doi: 10.1109/ICDSP.2014.6900688 http://doi.org/10.1109/ICDSP.2014.6900688

Bai JQ , Lian SG , Liu ZX , et al. , 2018 . Deep learning based robot for automatically picking up garbage on the grass . IEEE Trans Consum Electron , 64 ( 3 ): 382 - 389 . doi: 10.1109/TCE.2018.2859629 http://doi.org/10.1109/TCE.2018.2859629

Benjdira B , Khursheed T , Koubaa A , et al. , 2019 . Car detection using unmanned aerial vehicles: comparison between faster R-CNN and YOLOv3 . Proc 1st Int Conf on Unmanned Vehicle Systems-Oman , p. 1 - 6 . doi: 10.1109/UVS.2019.8658300 http://doi.org/10.1109/UVS.2019.8658300

Bochkovskiy A , Wang CY , Liao HYM , 2020 . YOLOv4: optimal speed and accuracy of object detection . https://arxiv.org/abs/2004.10934 https://arxiv.org/abs/2004.10934

Choi H , 2018 . Deep learning in nuclear medicine and molecular imaging: current perspectives and future directions . Nucl Med Mol Imag , 52 ( 2 ): 109 - 118 . doi: 10.1007/s13139-017-0504-7 http://doi.org/10.1007/s13139-017-0504-7

Dalal N , Triggs B , 2005 . Histograms of oriented gradients for human detection . Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition , p. 886 - 893 . doi: 10.1109/CVPR.2005.177 http://doi.org/10.1109/CVPR.2005.177

Ekins P , Gupta J , 2019 . Perspective: a healthy planet for healthy people . Glob Sustain , 2 : 1 - 9 . doi: 10.1017/sus.2019.17 http://doi.org/10.1017/sus.2019.17

Fei Y , Wang KCP , Zhang A , et al. , 2020 . Pixel-level cracking detection on 3D asphalt pavement images through deep-learning-based crackNet-V . IEEE Trans Intell Transp Syst , 21 ( 1 ): 273 - 284 . doi: 10.1109/TITS.2019.2891167 http://doi.org/10.1109/TITS.2019.2891167

Felzenszwalb P , McAllester D , Ramanan D , 2008 . A discriminatively trained, multiscale, deformable part model . IEEE Int Conf on Computer Vision and Pattern Recognition , p. 24 - 26 .

Fu ZH , Chen YW , Yong HW , et al. , 2019 . Foreground gating and background refining network for surveillance object detection . IEEE Trans Image Process , 28 ( 12 ): 6077 - 6090 . doi: 10.1109/TIP.2019.2922095 http://doi.org/10.1109/TIP.2019.2922095

Girshick R , 2015 . Fast R-CNN . Proc IEEE Int Conf on Computer Vision , p. 1440 - 1448 . doi: 10.1109/ICCV.2015.169 http://doi.org/10.1109/ICCV.2015.169

Girshick R , Donahue J , Darrell T , et al. , 2014 . Rich feature hierarchies for accurate object detection and semantic segmentation . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 580 - 587 . doi: 10.1109/CVPR.2014.81 http://doi.org/10.1109/CVPR.2014.81

Gural PS , 2019 . Deep learning algorithms applied to the classification of video meteor detections . Mon Not R Astron Soc , 489 ( 4 ): 5109 - 5118 . doi: 10.1093/mnras/stz2456 http://doi.org/10.1093/mnras/stz2456

Hannun AY , Rajpurkar P , Haghpanahi M , et al. , 2019 . Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network . Nat Med , 25 ( 1 ): 65 - 69 . doi: 10.1038/s41591-018-0268-3 http://doi.org/10.1038/s41591-018-0268-3

He KM , Zhang XY , Ren SQ , et al. , 2015 . Spatial pyramid pooling in deep convolutional networks for visual recognition . IEEE Trans Patt Anal Mach Intell , 37 ( 9 ): 1904 - 1916 . doi: 10.1109/TPAMI.2015.2389824 http://doi.org/10.1109/TPAMI.2015.2389824

He KM , Zhang XY , Ren SQ , et al. , 2016 . Identity mappings in deep residual networks . Proc 14th European Conf on Computer Vision , p. 630 - 645 . doi: 10.1007/978-3-319-46493-0-38 http://doi.org/10.1007/978-3-319-46493-0-38

Hong J , Fulton M , Sattar J , 2020 . A generative approach towards improved robotic detection of marine litter . Proc IEEE Int Conf on Robotics and Automation , p. 10525 - 10531 . doi: 10.1109/ICRA40945.2020.9197575 http://doi.org/10.1109/ICRA40945.2020.9197575

Horng GJ , Liu MX , Chen CC , 2020 . The smart image recognition mechanism for crop harvesting system in intelligent agriculture . IEEE Sens J , 20 ( 5 ): 2766 - 2781 . doi: 10.1109/JSEN.2019.2954287 http://doi.org/10.1109/JSEN.2019.2954287

Hsu WY , Lin WY , 2020 . Ratio-and-scale-aware YOLO for pedestrian detection . IEEE Trans Image Process , 30 : 934 - 947 . doi: 10.1109/TIP.2020.3039574 http://doi.org/10.1109/TIP.2020.3039574

Hussain E , Hasan M , Rahman A , et al. , 2021 . CoroDet: a deep learning based classification for COVID-19 detection using chest X-ray images . Chaos Sol Fract , 142 : 110495 . doi: 10.1016/j.chaos.2020.110495 http://doi.org/10.1016/j.chaos.2020.110495

Jambeck JR , Geyer R , Wilcox C , et al. , 2015 . Plastic waste inputs from land into the ocean . Science , 347 ( 6223 ): 768771 . doi: 10.1126/science.1260352 http://doi.org/10.1126/science.1260352

Karatzas P , Melagraki G , Ellis LJA , et al. , 2020 . Development of deep learning models for predicting the effects of exposure to engineered nanomaterials on Daphnia magna . Small , 16 ( 36 ): 2001080 . doi: 10.1002/smll.202001080 http://doi.org/10.1002/smll.202001080

Kim J , Mishra AK , Limosani R , et al. , 2019 . Control strategies for cleaning robots in domestic applications: a comprehensive review . Int J Adv Robot Syst , 16 ( 4 ): 1 - 21 . doi: 10.1177/1729881419857432 http://doi.org/10.1177/1729881419857432

Kong SH , Tian MJ , Qiu CL , et al. , 2021 . IWSCR: an intelligent water surface cleaner robot for collecting floating garbage . IEEE Trans Syst Man Cybern Syst , 51 ( 10 ): 6358 - 6368 . doi: 10.1109/TSMC.2019.2961687 http://doi.org/10.1109/TSMC.2019.2961687

Krizhevsky A , Sutskever I , Hinton GE , 2017 . ImageNet classification with deep convolutional neural networks . Commun ACM , 60 ( 6 ): 84 - 90 . doi: 10.1145/3065386 http://doi.org/10.1145/3065386

Laschi C , Mazzolai B , Cianchetti M , 2016 . Soft robotics: technologies and systems pushing the boundaries of robot abilities . Sci Robot , 41 ( 1 ): eaah3690 . doi: 10.1126/scirobotics.aah3690 http://doi.org/10.1126/scirobotics.aah3690

Li CY , Guo CL , Ren WQ , et al. , 2019 . An underwater image enhancement benchmark dataset and beyond . IEEE Trans Image Process , 29 : 4376 - 4389 . doi: 10.1109/TIP.2019.2955241 http://doi.org/10.1109/TIP.2019.2955241

Li HP , Xiong PF , An J , et al. , 2018 . Pyramid attention network for semantic segmentation . Proc British Machine Vision Conf , p. 285 .

Li XL , Tian MJ , Kong SH , et al. , 2020 . A modified YOLOv3 detection method for vision-based water surface garbage capture robot . Int J Adv Robot Syst , 17 ( 3 ): 1 - 11 . doi: 10.1177/1729881420932715 http://doi.org/10.1177/1729881420932715

Lin TY , Dollár P , Girshick R , et al. , 2017 . Feature pyramid networks for object detection . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 936 - 944 . doi: 10.1109/CVPR.2017.106 http://doi.org/10.1109/CVPR.2017.106

Liu W , Anguelov D , Erhan D , et al. , 2016 . SSD: single shot multibox detector . European Conf on Computer Vision , p. 21 - 37 . doi: 10.1007/978-3-319-46448-0-2 http://doi.org/10.1007/978-3-319-46448-0-2

Liu Z , Li JG , Shen ZQ , et al. , 2017 . Learning efficient convolutional networks through network slimming . Proc IEEE Int Conf on Computer Vision , p. 2755 - 2763 . doi: 10.1109/ICCV.2017.298 http://doi.org/10.1109/ICCV.2017.298

Lowe DG , 2004 . Distinctive image features from scale-invariant keypoints . Int J Comput Vis , 60 ( 2 ): 91 - 110 . doi: 10.1023/B:VISI.0000029664.99615.94 http://doi.org/10.1023/B:VISI.0000029664.99615.94

Mahler J , Pokorny FT , Hou B , et al. , 2016 . Dex-Net 1.0: a cloud-based network of 3D objects for robust grasp planning using a multi-armed bandit model with correlated rewards . Proc IEEE Int Conf on Robotics and Automation , p. 1957 - 1964 . doi: 10.1109/ICRA.2016.7487342 http://doi.org/10.1109/ICRA.2016.7487342

Mahler J , Liang J , Niyaz S , et al. , 2017 . Dex-Net 2.0: deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics . https://arxiv.org/abs/1703.09312 https://arxiv.org/abs/1703.09312

Mahler J , Matl M , Liu XY , et al. , 2018 . Dex-Net 3.0: computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning . Proc IEEE Int Conf on Robotics and Automation , p. 5620 - 5627 . doi: 10.1109/ICRA.2018.8460887 http://doi.org/10.1109/ICRA.2018.8460887

Mahler J , Matl M , Satish V , et al. , 2019 . Learning ambidextrous robot grasping policies . Sci Robot , 4 ( 26 ): eaau4984 . doi: 10.1126/scirobotics.aau4984 http://doi.org/10.1126/scirobotics.aau4984

Mhalla A , Chateau T , Gazzah S , et al. , 2019 . An embedded computer-vision system for multi-object detection in traffic surveillance . IEEE Trans Intell Transp Syst , 20 ( 11 ): 4006 - 4018 . doi: 10.1109/TITS.2018.2876614 http://doi.org/10.1109/TITS.2018.2876614

Ming X , Wei FY , Zhang T , et al. , 2022 . Group sampling for scale invariant face detection . IEEE Trans Patt Anal Mach Intell , 44 ( 2 ): 985 - 1001 . doi: 10.1109/TPAMI.2020.3012414 http://doi.org/10.1109/TPAMI.2020.3012414

Molchanov P , Mallya A , Tyree S , et al. , 2019 . Importance estimation for neural network pruning . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition , p. 11256 - 11264 . doi: 10.1109/CVPR.2019.01152 http://doi.org/10.1109/CVPR.2019.01152

Ojala T , Pietikäinen M , Maenpaa T , 2002 . Multiresolution gray-scale and rotation invariant texture classification with local binary patterns . IEEE Trans Patt Anal Mach Intell , 24 ( 7 ): 971 - 987 . doi: 10.1109/TPAMI.2002.1017623 http://doi.org/10.1109/TPAMI.2002.1017623

Ostle C , Thompson RC , Broughton D , et al. , 2019 . The rise in ocean plastics evidenced from a 60-year time series . Nat Commun , 10 ( 1 ): 1622 . doi: 10.1038/s41467-019-09506-1 http://doi.org/10.1038/s41467-019-09506-1

Park JH , Hwang HW , Moon JH , et al. , 2019 . Automated identification of cephalometric landmarks: Part 1—comparisons between the latest deep-learning methods YOLOV3 and SSD . Angle Orthod , 89 ( 6 ): 903 - 909 . doi: 10.2319/022019-127.1 http://doi.org/10.2319/022019-127.1

Prabakaran V , Elara MR , Pathmakumar T , et al. , 2018 . Floor cleaning robot with reconfigurable mechanism . Autom Constr , 91 : 155 - 165 . doi: 10.1016/j.autcon.2018.03.015 http://doi.org/10.1016/j.autcon.2018.03.015

Pu SL , Zhao W , Chen WJ , et al. , 2021 . Unsupervised object detection with scene-adaptive concept learning . Front Inform Technol Electron Eng , 22 ( 5 ): 638 - 651 . doi: 10.1631/FITEE.2000567 http://doi.org/10.1631/FITEE.2000567

Redmon J , Farhadi A , 2017 . YOLO9000: better, faster, stronger . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 6517 - 6525 . doi: 10.1109/CVPR.2017.690 http://doi.org/10.1109/CVPR.2017.690

Redmon J , Farhadi A , 2018 . YOLOv3: an incremental improvement . https://arxiv.org/abs/1804.02767 https://arxiv.org/abs/1804.02767

Redmon J , Divvala S , Girshick R , et al. , 2016 . You only look once: unified, realtime object detection . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 779 - 788 . doi: 10.1109/CVPR.2016.91 http://doi.org/10.1109/CVPR.2016.91

Ren SQ , He KM , Girshick RB , et al. , 2015 . Faster R-CNN: towards real-time object detection with region proposal networks . Proc Annual Conf on Neural Information Processing Systems , p. 91 - 99 .

Simonyan K , Zisserman A , 2015 . Very deep convolutional networks for large-scale image recognition . https://arxiv.org/abs/1409.1556 https://arxiv.org/abs/1409.1556

Song ZG , Zou SM , Zhou WX , et al. , 2020 . Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning . Nat Commun , 11 ( 1 ): 4294 . doi: 10.1038/s41467-020-18147-8 http://doi.org/10.1038/s41467-020-18147-8

Szegedy C , Liu W , Jia YQ , et al. , 2015 . Going deeper with convolutions . Proc IEEE Conf on Computer Vision and Pattern Recognition , p. 1 - 9 . doi: 10.1109/CVPR.2015.7298594 http://doi.org/10.1109/CVPR.2015.7298594

Tian MJ , Li XL , Kong SH , et al. , 2021 . Pruning-based YOLOv4 algorithm for underwater gabage detection . Proc 40th Chinese Control Conf , p. 4008 - 4013 . doi: 10.23919/CCC52363.2021.9550592 http://doi.org/10.23919/CCC52363.2021.9550592

Tschandl P , 2020 . Problems and potentials of automated object detection for skin cancer recognition . JAMA Dermatol , 156 ( 1 ): 23 - 24 . doi: 10.1001/jamadermatol.2019.3360 http://doi.org/10.1001/jamadermatol.2019.3360

Valdenegro-Toro M , 2019 . Deep neural networks for marine debris detection in sonar images . https://arxiv.org/abs/1905.05241 https://arxiv.org/abs/1905.05241

Viola P , Jones M , 2001 . Rapid object detection using a boosted cascade of simple features . Proc IEEE Computer Society Conf on Computer Vision and Pattern Recognition , p. 511 - 518 . doi: 10.1109/CVPR.2001.990517 http://doi.org/10.1109/CVPR.2001.990517

Wang CY , Liao HYM , Wu YH , et al. , 2020 . CSPNet: a new backbone that can enhance learning capability of CNN . Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition Workshops , p. 1571 - 1580 . doi: 10.1109/CVPRW50498.2020.00203 http://doi.org/10.1109/CVPRW50498.2020.00203

Whitehill J , Omlin CW , 2006 . Haar features for FACS AU recognition . Proc 7th Int Conf on Automatic Face and Gesture Recognition , p. 5 - 101 . doi: 10.1109/FGR.2006.61 http://doi.org/10.1109/FGR.2006.61

Xu M , Karuppusamy NS , Kang BY , 2017 . A novel design to improve the cooperative ability of the multi-cleaning robot in the unknown environment . Adv Sci Lett , 23 ( 10 ): 9557 - 9560 . doi: 10.1166/asl.2017.9746 http://doi.org/10.1166/asl.2017.9746

浏览量

137

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion

UI layers merger: merging UI layers via visual learning and boundary prior

Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance

Detection of engineering vehicles in high-resolution monitoring images