[1]鲁 濠,王淑青,鲁东林,等. 基于改进YOLOv5的小龙虾品质检测方法[J].湖北工业大学学报,2023,(4):76-81.
　LU Hao,WANG Shuqing,LU Donglin,et al. Quality Detection Method of Crayfish based on Improved YOLOv5[J].,2023,(4):76-81.
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 基于改进YOLOv5的小龙虾品质检测方法()
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《湖北工业大学学报》[ISSN:1003-4684/CN:42-1752/Z]

卷:
期数:
2023年第4期
页码:
76-81
栏目:
湖北工业大学学报
出版日期:
2023-08-30

文章信息/Info

Title:
 Quality Detection Method of Crayfish based on Improved YOLOv5
文章编号:
1003-4684(2023)04-0076-06
作者:
 鲁 濠1;  王淑青1;  鲁东林1;  张子蓬2;  汤 璐1
 1 湖北工业大学电气与电子工程学院， 湖北 武汉 430068；
 2 武汉奋进智能机器有限公司， 湖北 武汉 430074
Author(s):
 LU Hao1;  WANG Shuqing1;  LU Donglin1;  ZHANG Zipeng2;  TANG Lu1
 1 School of Electrical and Electronic Engin.,Hubei Univ.of Tech.,Wuhan 430068,China;
 2 Wuhan Fenjin Intelligent Machine Co.,Ltd, Wuhan 430074, China
关键词:
 ; 目标检测;  小龙虾检测;  YOLOv5网络;  深度学习
Keywords:
 ; crayfish detection;  target detection;  yolov5 network;  deep learning
分类号:
TP391.41
文献标志码:
A
摘要:
 小龙虾质量检测是其生产加工中的重要环节，针对当前目标检测模型在不同数据集上存在拟合性能不佳、冗余错误等问题，提出了一种基于改进YOLOv5网络的小龙虾质量检测方法。首先在主干网络采用DP-Conv卷积改进CNN卷积，利用高维过度参数化提高拟合效果和检测速度。其次在残差块Res_unit中嵌入SENet模块加强上下文的语义联系，减少冗余信息的干扰。最后设计一种小龙虾数据采集方法。与3种目标检测算法实验对比，结果表明，改进后的算法检测精度达到97%，速度为60帧/s，模型性能优于其他3种算法，具有高检测精度和高速度的应用效果。
Abstract:
 The quality detection of crayfish is an important link in its production and processing. Aiming at the problems of poor fitting performance and redundant errors of the current target detection model on different data sets, a method for quality detection of crayfish based on the improved YOLOv5 network is proposed. . First, the DP-Conv convolution is used in the backbone network to improve the CNN convolution, and high-dimensional over-parameterization is used to improve the fitting effect and detection speed. Secondly, the SENet module is embedded in the residual block Res_unit to strengthen the semantic connection of the context and reduce the interference of redundant information. Finally, a data collection method for crayfish is designed. Comparing the proposed algorithm with three kinds of target detection algorithms, the results show that the improved algorithm has a detection accuracy of 97%, a speed of 60 frames/s, and the model performance is better than the other three algorithms, having high testing accuracy and application effect.

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备注/Memo

备注/Memo:
 ［收稿日期］ 2022-03-30
［第一作者］ 鲁 濠(1999-)，男，湖北黄冈人，湖北工业大学硕士研究生，研究方向为自动化与图像处理。
［通信作者］ 张子蓬(1968-)，男，山东武城人，工学博士，湖北工业大学副教授，研究方向为机器视觉与机器人优化算法。
更新日期/Last Update: 2023-08-26