[1]顾德英, 陈龙, 李文超, 等. 基于深度学习的复杂图案印花织物疵点检测[J]. 棉纺织技术, 2022, 50(3): 14-18.
GU Deying, CHEN Long, LI Wenchao, et al. Complex pattern printed fabric defect detection based on deep learning[J]. Cotton Textile Technology, 2022,50 (3) : 14-18.
[2]崔春杰, 景浩田, 尹中信. 深度学习算法在织物疵点识别中的应用研究[J]. 化纤与纺织技术, 2021, 50(1):69-71.
CUI Chunjie, JING Haotian, YIN Zhongxin. Application of deep learning algorithm in fabric defect recognition[J]. Chemical Fiber & Textile Technology, 2021, 50 (1) : 69-71.
[3]李一鸣, 王潇. 基于YOLOv5s模型的轧钢表面缺陷检测[J]. 制造业自动化, 2021, 43(11):117-119.
LI Yiming, WANG Xiao. Surface defect detection of rolled steel based on the YOLOv5s model [J]. Manufacturing Automation, 2021,43 (11) : 117-119.
[4] 俞新星,任勇,支佳雯.织物表面疵点检测方法的设计与实现[J].现代纺织技术,2021,29(1):62-67.
YU Xinxing, REN Yong, ZHI Jiawen. Design and implementation of defect detection method for fabric surface[J]. Advanced Textile Technology, 2021, 29 ( 1 ) : 62-67.
[5]张团善, 石玮. 噪声干扰下的防羽布疵点检测算法[J]. 西安工程大学学报, 2020, 34(1):14-19.
ZHANG Tuanshan, SHI Wei. Anti-feather fabric defect detection algorithm under noise interference[J]. Journal of Xi'an Polytechnic University, 2020, 34(1): 14-19.
[6]王恩芝, 张团善, 刘亚. 基于改进Yolo v5的织物缺陷检测方法[J]. 轻工机械, 2022, 40(2):54-60.
WANG Enzhi, ZHANG Tuanshan, LIU Ya. Fabric defect detection method based on improved Yolo v5[J]. Light Industry Machinery, 2022, 40 (2) : 54-60.
[7]郭波, 吕文涛, 余序宜, 等. 基于改进YOLOv5模型的织物疵点检测算法[J]. 浙江理工大学学报(自然科学版), 2022, 47(5):755-763.
GUO Bo, LÜ Wentao, YU Xuyi, et al. Fabric defect detection algorithm based on improved YOLOv5 model[J]. Journal of Zhejiang University of Technology (Natural Sciences), 2022,47 (5) : 755-763.
[8]XUE Z Y, LIN H F, WANG F. A small target forest fire detection model based on YOLOv5 improvement[J]. Forests, 2022, 13(8): 1332.
[9]DENG T M, LIU X H, MAO G T. Improved YOLOv5 based on hybrid domain attention for small object detection in optical remote sensing images[J]. Electronics, 2022, 11(17): 2657.
[10]LEI F, TANG F F, LI S H. Underwater target detection algorithm based on improved YOLOv5[J]. Journal of Marine Science and Engineering, 2022, 10(3): 310.
[11]周文明, 周建, 潘如如. 应用遗传算法优化Gabor滤波器的机织物疵点检测[J]. 东华大学学报(自然科学版), 2020, 46(4):535-541.
ZHOU Wenming, ZHOU Jian, PAN Ruru. Application of genetic algorithm to optimize Gabor filter for woven fabric defect detection[J]. Journal of Donghua University (Natural Science), 2020,46 (4) : 535-541.
[12]DLAMINI S, KAO C Y, SU S L, et al. Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model[J]. Textile Research Journal, 2022, 92(5-6): 675-690.
[13]LIAN J W, HE J H, NIU Y, et al. Fast and accurate detection of surface defect based on improved YOLOv4[J]. Assembly Automation, 2022, 42(1): 134-146.
[14]FU H X, SONG G Q, WANG Y C. Improved YOLOv4 marine target detection combined with CBAM[J]. Symmetry, 2021, 13(4): 623.
[15]XUE M F, CHEN M H, PENG D L, et al. One spatio-temporal sharpening attention mechanism for light-weight YOLO models based on sharpening spatial attention[J]. Sensors, 2021, 21(23): 7949.
[16]SOZZI M, CANTALAMESSA S, COGATO A, et al. Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms[J]. Agronomy, 2022, 12(2): 319.
[17]YAN P C, SUN Q S, YIN N N, et al. Detection of coal and gangue based on improved YOLOv5. 1 which embedded scSE module[J]. Measurement, 2022, 188: 110530.
[18]ZHAO Z Y, YANG X X, ZHOU Y C, et al. Real-time detection of particleboard surface defects based on improved YOLOV5 target detection[J]. Scientific Reports, 2021, 11(1): 1-15.
[19]LV H H, YAN H B, LIU K Y, et al. YOLOv5-AC: Attention mechanism-based lightweight YOLOv5 for track pedestrian detection[J]. Sensors, 2022, 22(15): 5903.
|