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

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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
  Enhanced Publication
- 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
- 纸质出版日期：2022-08-23，
  
  收稿日期：2021-10-01，
  
  录用日期：2022-03-07
- Accepted：
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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

目标检测水环境垃圾清理机器人改进YOLOv4

Keywords

Object detectionAquatic environmentGarbage cleaning robotModified YOLOv4

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