基于YOLOv8-S的偏光片表面缺陷检测算法

doi:10.3778/j.issn.1002-8331.2401-0382

摘要/Abstract

摘要： 随着偏光片市场不断扩大，应用愈发广泛，对偏光片的生产要求也愈加严格。针对在偏光片表面缺陷检测中，存在缺陷形态复杂、小尺寸缺陷容易误检漏检的问题，提出一种基于YOLOv8-S偏光片表面缺陷检测改进算法。使用DCNv3替换主干网C2f模块中的普通卷积，同时结合EMA注意力机制，构建DEC2f特征提取模块，提升主干网对复杂缺陷的特征提取能力。基于特征细化模块构建轻量跨尺度特征细化融合模块（LCFRFM），提升通道净化能力并降低参数量，有效跨尺度融合主干网浅层特征。引入ConvMixer Layer构建CMC2f预测头，更大的预测视野带来更强的小尺寸缺陷检测能力。使用SIoU替换CIoU作为边界框回归损失函数，使用AdamW替换SGD作为网络训练时的优化器，提升检测精度和训练收敛速度。实验结果表明，该算法相比YOLOv8-S在mAP50和mAP50：95上分别提升了2.4和2.9个百分点，证明了提出算法的有效性。

关键词: 偏光片表面, 缺陷检测, 特征提取, 跨尺度特征融合, YOLOv8

Abstract: As the polarizer market continues to expand, the application is more and more extensive, the production requirements for polarizer are also more and more stringent. Aiming at the problems of complex defect morphology, small-size defects detection false and missed in polarizer surface defect detection, this paper proposes an improved algorithm based on YOLOv8-S polarizer surface defect detection. DCNv3 is used to replace the ordinary convolution in the C2f module of the backbone network, and at the same time, combining with the EMA , the DEC2f feature extraction module is constructed, which improves the feature extraction capability of the backbone network for complex defects. Lightweight cross-scale feature refinement fusion module (LCFRFM) is constructed based on the feature refinement module to improve the channel purification capability and reduce the number of parameters, and effectively cross-scale fusion of shallow features in the backbone network. The ConvMixer Layer is introduced to construct the CMC2f prediction head, and the larger prediction field of view brings stronger small-size defect detection capability. SIoU is used to replace CIoU as the bounding box regression loss function, and AdamW is used to replace SGD as the optimizer during network training to improve the detection accuracy and training convergence speed. The experimental results show that the proposed algorithm improves 2.4 and 2.9 percentage points on mAP50 and mAP50:95, respectively, compared to YOLOv8-S, which proves the effectiveness of the proposed algorithm.

Key words: polarizer surface, defect detection, feature extraction, cross-scale feature fusion, YOLOv8

盛威, 周永霞, 陈俊杰, 赵平. 基于YOLOv8-S的偏光片表面缺陷检测算法[J]. 计算机工程与应用, 2025, 61(6): 128-140.

SHENG Wei, ZHOU Yongxia, CHEN Junjie, ZHAO Ping. Polarizer Surface Defect Detection Algorithm Based on YOLOv8-S[J]. Computer Engineering and Applications, 2025, 61(6): 128-140.

参考文献

[1] YOON Y G, LEE S L, CHUNG C W, et al. An effective defect inspection system for polarized film images using image segmentation and template matching techniques[J]. Computers & Industrial Engineering, 2008, 55(3): 567-583.
[2] 黄广俊, 邓元龙. 融合改进LBP和SVM的偏光片外观缺陷检测与分类[J]. 计算机工程与应用, 2020, 56(22): 251-255.
HUANG G J, DENG Y L. Polarizer visual defect detection and classification based on improved LBP and SVM algorithm[J]. Computer Engineering and Applications, 2020, 56(22): 251-255.
[3] LIU R, SUN Z Y, WANG A H, et al. Lightweight efficient network for defect classification of polarizers[J]. Con-currency and Computation: Practice and Experience, 2020, 32(11): e5563.
[4] LIU R, SUN Z Y, WANG A H, et al. Real-time defect detection network for polarizer based on deep learning[J]. Journal of Intelligent Manufacturing, 2020, 31: 1813-1823.
[5] 夏禹, 肖金球, 翁玉尚. 基于改进Faster-RCNN的偏光片表面缺陷检测[J]. 光学技术, 2021, 47(6): 695-702.
XIA Y, XIAO J Q, WENG Y S. Surface defect detection of polarizer based on improved Faster-RCNN[J]. Optical Technique, 2021, 47(6): 695-702.
[6] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, realtime object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 779-788.
[7] REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017: 7263-7271.
[8] REDMON J, FARHADI A. YOLOv3: an incremental improvement[J]. arXiv:1804.02767, 2018.
[9] BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[J]. arXiv:2004.10934, 2020.
[10] GE Z, LIU S, WANG F, et al. YOLOx: exceeding YOLO series in 2021[J]. arXiv:2107.08430, 2021.
[11] LI C, LI L, JIANG H, et al. YOLOv6: a single-stage object detection framework for industrial applications[J]. arXiv:2209.02976, 2022.
[12] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 7464-7475.
[13] 陈乐, 周永霞, 祖佳贞. 改进YOLOX-S的偏光片表面缺陷检测算法[J]. 计算机工程与应用, 2024, 60(2): 295-303.
CHEN L, ZHOU Y X, ZU J Z. Surface defect detection of polarizer based on improved YOLOX-S algorithm[J]. Computer Engineering and Applications, 2024, 60(2): 295-303.
[14] WANG W, DAI J, CHEN Z, et al. Internimage: exploring large-scale vision foundation models with deformable convolutions[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 14408-14419.
[15] OUYANG D, HE S, ZHANG G, et al. Efficient multi-scale attention module with cross-spatial learning[C]//Proceedings of 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023: 1-5.
[16] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[17] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 13713-13722.
[18] WANG Q, WU B, ZHU P, et al. ECA-Net: efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 11534-11542.
[19] HOWARD A G, ZHU M, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017.
[20] LOU H, DUAN X, GUO J, et al. DC-YOLOv8: smallsize object detection algorithm based on camera sensor[J]. Electronics, 2023, 12(10): 2323.
[21] LIANG S H, HUANG W M, XIAO Y N. Enhanced YOLOv7-based construction site environmental detection method[C]//Proceedings of the 2023 4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), 2023: 277-283.
[22] TROCKMAN A, KOLTER J Z. Patches are all you need?[J]. arXiv:2201.09792, 2022.
[23] ZHU X, LYU S, WANG X, et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 2778-2788.
[24] GEVORGYAN Z. SIoU loss: more powerful learning for bounding box regression[J]. arXiv:2205.12740, 2022.
[25] LOSHCHILOV I, HUTTER F. Decoupled weight decay regularization[J]. arXiv:1711.05101, 2017.
[26] KINGMA D P, BA J. ADAM: a method for stochastic optimization[J]. arXiv:1412.6980, 2014.
[27] ZHANG Y F, REN W, ZHANG Z, et al. Focal and efficient IOU loss for accurate bounding box regression[J]. Neurocomputing, 2022, 506: 146-157.
[28] REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 658-666.
[29] ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020: 12993-13000.
[30] ZHANG S, CHI C, YAO Y, et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 9759-9768.
[31] DUAN K, BAI S, XIE L, et al. Centernet: keypoint triplets for object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 6569-6578.
[32] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE Inter-National Conference on Computer Vision, 2017: 2980-2988.
[33] FENG C, ZHONG Y, GAO Y, et al. TOOD: task-aligned one-stage object detection[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021: 3490-3499.
[34] ZHU C, HE Y, SAVVIDES M. Feature selective anchorfree module for single-shot object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 840-849.
[35] TIAN Z, SHEN C, CHEN H, et al. FCOS: fully convolu-tional one-stage object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 9627-9636.
[36] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015.
[37] CAI Z, VASCONCELOS N. Cascade R-CNN: delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 6154-6162.
[38] ZHANG H, WANG Y, DAYOUB F, et al. VarifocalNet: an IoU-aware dense object detector[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 8514-8523.
[39] XU S, WANG X, LV W, et al. PP-YOLOE: an evolved version of YOLO[J]. arXiv:2203.16250, 2022.

编辑推荐 0

Metrics

阅读次数

全文

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	57

	来源	本网站

	次数	57
	比例	100%

摘要

最新录用	在线预览	正式出版

0	0	39

	来源	本网站

	次数	39
	比例	100%