[1]刘瑞昊,于振中,孙 强.改进多尺度特征融合的工业现场目标检测算法[J].机械与电子,2022,(11):40-45.
　LIU Ruihao,YU Zhenzhong,SUN Qiang.Improved Multi-scale Feature Fusion for Industrial Field Object Detection Algorithm[J].Machinery & Electronics,2022,(11):40-45.
点击复制

改进多尺度特征融合的工业现场目标检测算法()
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2022年11期
页码:
40-45
栏目:
自动控制与检测
出版日期:
2022-11-25

文章信息/Info

Title:
Improved Multi-scale Feature Fusion for Industrial Field Object Detection Algorithm
文章编号:
1001-2257 ( 2022 ) 11-0040-06
作者:
刘瑞昊 1 ; 于振中 2 ; 孙 强 2
1. 江南大学物联网工程学院,江苏 无锡 214122 ;
2. 哈工大机器人国际创新研究院人工智能研究所,安徽 合肥 230601
Author(s):
LIU Ruihao1 ;  YU Zhenzhong2 ;  SUN Qiang2
( 1.School of Internet of Things Engineering , Jiangnan University , Wuxi 214122 , China ; 2.Institute of Artificial Intelligence , HRG International Institute for Research and Innovation , Hefei 230601 , China )
关键词:
特征融合; 目标检测;  YOLOv3 算法; 安全帽检测; 安全绳检测
Keywords:
feature fusion ;  object detection ;  YOLOv3 algorithm ;  helmet detection ;  safety rope detection
分类号:
TP391
文献标志码:
A
摘要:
为了提高工业现场等复杂场景下的小目标检测的准确率,降低工业现场的安全事故发生率,基于 YOLOv3 提出了一种改进多尺度特征融合方法。该方法增加了Inception _ shortcut 模块,优化网络的输出宽度,使用工业现场的监控视频作为数据集以及利用 k-means 算法对检测目标重新聚类,引入了 PANet 多尺度特征融合结构,精简了 YOLOv3 的网络检测输出层。在创建工业现场安全帽、安全绳数据集 FHPD 、FSRPD 以及 PASCAL VOC2007 数据集上的实验结果表明,改进算法的 mAP 比原始 YOLOv3 提高了许多。改进的多尺度特征网络融合增加了参数,但检测速度仍满足算法的实时性要求。
Abstract:
To improve the accuracy of small target detection in complex scenes such as industrial sites and reduce the incidence of safety accidents in industrial sites , an improved multi-scale feature fusion method is proposed based on YOLOv3 algorithm.The method added the Inception _ shortcut module to optimize the output width of the network , the surveillance videos of the industrial site were used as the data set and?k-means algorithm was used to re-cluster the detection targets , the PANet multi-scale feature fusion structure was introduced , and the network detection output layer of YOLOv3 was simplified.The experimental results on the industrial field helmets , safety rope datasets FHPD , FSRPD and PASCAL VOC2007 datasets showed that the mAP of the proposed algorithm is higher than the original YOLOv3. The improved multi-scale feature network fusion increases the parameters , but the detection speed still meets the real-time requirements of the algorithm.

参考文献/References:

[ 1 ] NWAKANMA I C , ISLAM F B , MAHARANI M P , et al.Detection and classification of human activity for emergency response in smart factory shop floor [ J ] .Applied sciences , 2021 , 11 ( 8 ): 1-19.

[ 2 ] 王兵,李文璟,唐欢 . 改进 YOLOv3 算法及其在安全帽检 测中的应用[ J ] . 计算机工程与应用, 2020 , 56 ( 9 ): 33-40.
[ 3 ] KRIZHEVSKY A , SUTSKEVER I , HINTON G E. Imagenet classification with deep convolutional neural networks [ J ] .Advances in neural information processing systems , 2012 , 25 : 1097-1105.
[ 4 ] SZEGEDY C , LIU W , JIA Y Q , et al.Going deeper with convolutions [ C ]// 2015 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ).New York : IEEE , 2015 : 1-9.
[ 5 ] HE K , ZHANG X , REN S , et al.Deep residual learning for image recognition [ C ]// 2016 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) . New York : IEEE , 2016 : 770-778.
[ 6 ] GIRSHICK R , DONAHUE J , DARRELL T , et al. Rich feature hierarchies for accurate object detection and semantic segmentation [ C ]// 2014 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) .New York : IEEE , 2014 : 580-587.
[ 7 ] HE K M , ZHANG X Y , REN S Q , et al.Spatial pyramid pooling in deep convolutional networks for visual recognition [ J ] .IEEE Transactions on pattern analysis and machine intelligence , 2015 , 37 ( 9 ): 1904-1916.
[ 8 ] GIRSHICK R.Fast R-CNN [ C ] ∥2015 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) .New York : IEEE , 2015 : 1440-1448.
[ 9 ] REN S Q , HE K M , GIRSHICK R , et al.Faster R-CNN : towards real-time object detection with region proposal networks [ J ] .Advances in neural information processing systems , 2015 , 28 : 91-99.
[ 10 ] REDMON J , DIVVALA S , GIRSHICK R , et al.You only look once : unified , real-time object detection[ C ] ∥2016 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) .New York : IEEE , 2016 : 779-788.
[ 11 ] LIU W , ANGUELOV D , ERHAN D , et al.SSD : single shot multibox detector[ C ] ∥European conference on conputer Vision.Cham : Springer , 2016 : 21-37.
[ 12 ] REDMON J , FARHADI A.YOLO9000 : better , faster , stronger [ C ] ∥2017 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) .New York : IEEE , 2017 : 6517-6525.
[ 13 ] REDMON J , FARHADI A.YOLOv3 : An incremental improvement[ C ] ∥2018 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) .New York : IEEE , 2018 : 3324-3327.
[ 14 ] WU F , JIN G Q , GAO M Y , et al. Helmet detection based on improved YOLOv3 deep model [ C ] ∥2019 IEEE 16th International Conference on Networking , Sensing and Control( ICNSC ) .New York : IEEE , 2019 : 363-368.
[ 15 ] LIN F Y , ZHENG X , WU Q. Small object detection in aerial view based on improved YOLOv3 neural network[ C ] ∥2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications( AEECA ) .New York : IEEE , 2020 : 522-525.
[ 16 ] 卢官有,顾正弘 . 改进的 YOLOv3 安检包裹中危险品检测算法[ J ] . 计算机应用与软件, 2021 , 38 ( 1 ): 197-204.
[ 17 ] 冯国臣,陈艳艳,陈宁,等 . 基于机器视觉的安全帽自动识别技术研究[ J ] . 机械设计与制造工程, 2015 , 44( 10 ): 39-42.
[ 18 ] 刘晓慧,叶西宁 . 肤色检测和Hu矩在安全帽识别中的应用[ J ] . 华东理工大学学报, 2014 , 40 ( 3 ): 365-370.
[ 19 ] SUN X M , XU K G , WANG S , et al.Detection and tracking of safety helmet in factory environment [ J ] . Measurement science and technology , 2021 , 32( 10 ): 105406.
[ 20 ] 王雨生,顾玉宛,庄丽华,等 . 复杂姿态下的安全帽佩戴检测方法研究[ J ] . 计算机工程与应用,2022 , 58( 1 ): 190-196.
[ 21 ] LIN T Y , DOLLáR P , GIRSHICK R , et al.Feature pyramid networks for object detection [ C ] ∥2017 IEEE Conference on Computer Vision and Pattern Recognition( CVPR ) .New York : IEEE , 2017 : 936-944.

相似文献/References:

[1]章 巍,严鸿平,钱和平,等.基于图像异类特征融合的基建工地现场安全智能预警系统[J].机械与电子,2021,(04):76.
　ZHANG Wei,YAN Hongping,QIAN Heping,et al.On-site Safety Intelligent Early Warning System of Infrastructure Construction Site Based on Image Heterogeneous Feature Fusion[J].Machinery & Electronics,2021,(11):76.
[2]卞越洋,高晓科,张伟军.机器人带电作业中的视觉定位与优化策略[J].机械与电子,2021,(05):57.
　BIAN Yueyang,GAO Xiaoke,ZHANG Weijun.Vision Positioning and Optimization Strategyin Robot Live Work[J].Machinery & Electronics,2021,(11):57.
[3]范 涛,王明泉,张俊生,等.基于轻量化 YOLOv4 的轮毂内部缺陷检测算法[J].机械与电子,2023,41(02):3.
　FAN Tao,WANG Mingquan,ZHANG Junsheng,et al.Internal Defect Detection Algorithm of Wheel Hub Based on Lightweight YOLOv4[J].Machinery & Electronics,2023,41(11):3.
[4]李文波,王 玉,王明泉,等.瞬态场景下基于光流法的红外视频插帧算法研究[J].机械与电子,2024,42(04):15.
　LI Wenbo,WANG Yu,WANG Mingquan,et al.Research on Infrared Video Frame Interpolation Algorithm Based on Optical Flow Method in Transient Scene[J].Machinery & Electronics,2024,42(11):15.
[5]张晋钊,刘新妹,等.改进 YOLOv5s 的 PCB 元器件检测技术[J].机械与电子,2024,42(10):35.
　ZHANG Jinzhao,LIU Xinmei,et al.Improved YOLOv5s Component Detection Technology for PCB[J].Machinery & Electronics,2024,42(11):35.
[6]黄心玥,王明泉,耿宇杰,等.基于 YOLOv8 的铝合金铸造轮毂缺陷检测算法研究与分析[J].机械与电子,2025,(02):26.
　HUANG Xinyue,WANG Mingquan,GENG Yujie,et al.Research and Analysis of Defect Detection Algorithm for Aluminum Alloy Casting Wheels Based on YOLOv8[J].Machinery & Electronics,2025,(11):26.
[7]郑卓纹,吴攀超,王婷婷,等.基于轻量化目标检测算法的指针仪表读数识别[J].机械与电子,2025,(05):10.
　ZHENG Zhuowen,WU Panchao,WANG Tingting,et al.Pointer Gauge Reading Recognition Based on Lightweight Target Detection Algorithm[J].Machinery & Electronics,2025,(11):10.

备注/Memo

备注/Memo:
收稿日期: 2022-03-01
基金项目:安徽省科技重大专项( 202003a05020015 )
作者简介:刘瑞昊 ( 1997- ),男,安徽合肥人,硕士研究生,研究方向为人工智能、目标检测等;于振中 ( 1980- ),男,安徽宿州人,博士,副教授,研究方向为人工智能、机器人控制技术、特种机器人技术等;孙 强 ( 1993- ),男,江苏无锡人,硕士,工程师,研究方向为机器视觉、工业机器人。
更新日期/Last Update: 2022-12-16