Long-tailed image defect detection based on gradient-guide weighted-deferred negative gradient decay loss

doi:10.11772/j.issn.1001-9081.2022091413

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (10): 3267-3274.DOI: 10.11772/j.issn.1001-9081.2022091413

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles Next Articles

Long-tailed image defect detection based on gradient-guide weighted-deferred negative gradient decay loss

Wei LI(), Sixin LIANG, Jianzhou ZHANG

College of Computer Science，Sichuan University，Chengdu Sichuan 610065，China

Received:2022-09-26 Revised:2022-12-04 Accepted:2022-12-12 Online:2023-03-07 Published:2023-10-10
Contact: Wei LI
About author:LI Wei， born in 1998， M. S. candidate. His research interests include defect detection， object detection.
LIANG Sixin， born in 1995， Ph. D. candidate. His researchinterests include machine vision， camera calibration， medical image processing.
ZHANG Jianzhou， born in 1962， Ph. D.， professor. His research interests include machine vision， computer vision.
Supported by:
China Postdoctoral Science Foundation(2022M712235);Postdoctoral Research and Development Foundation of Sichuan University(2022SCU12074)

基于梯度引导加权‒延迟负梯度衰减损失的长尾图像缺陷检测

李巍(), 梁斯昕, 张建州

四川大学计算机学院，成都 610065

通讯作者: 李巍
作者简介:李巍（1998—），男，江苏泰州人，硕士研究生，主要研究方向：缺陷检测、目标检测. wana@stu.scu.edu.cn
梁斯昕（1995—），男，云南曲靖人，博士研究生，主要研究方向：机器视觉、摄像机标定、医学图像处理
张建州（1962—），男，河北邯郸人，教授，博士，主要研究方向：机器视觉、计算机视觉。
基金资助:
中国博士后科学基金资助项目(2022M712235);四川大学专职博士后研发基金资助项目(2022SCU12074)

Abstract

Abstract:

Aiming at the problem that the current image defect detection models have poor detection effect on tail categories in long-tail defect datasets， a GGW-DND Loss （Gradient-Guide Weighted-Deferred Negative Gradient decay Loss） was proposed. First， the positive and negative gradients were re-weighted according to the cumulative gradient ratio of the classification nodes in the detector in order to reduce the suppressed state of tail classifier. Then， once the model was optimized to a certain stage， the negative gradient generated by each node was sharply reduced to enhance the generalization ability of the tail classifier. Experimental results on the self-made image defect dataset and NEU-DET （NEU surface defect database for Defect dEtection Task） show that the mean Average Precision （mAP） for tail categories of the proposed loss is better than that of Binary Cross Entropy Loss （BCE Loss）， the former is increased by 32.02 and 7.40 percentage points respectively， and compared with EQL v2 （EQualization Loss v2）， the proposed loss has the mAP increased by 2.20 and 0.82 percentage points respectively， verifying that the proposed loss can effectively improve the detection performance of the network for tail categories.

Key words: long-tail dataset, cumulative gradient ratio, weighted loss, image defect detection, Convolutional Neural Network (CNN)

摘要：

针对目前图像缺陷检测模型对长尾缺陷数据集中尾部类检测效果较差的问题，提出一个基于梯度引导加权?延迟负梯度衰减损失（GGW-DND Loss）。首先，根据检测器分类节点的累积梯度比值分别对正负梯度重新加权，减轻尾部类分类器的受抑制状态；其次，当模型优化到一定阶段时，直接降低每个节点产生的负梯度，以增强尾部类分类器的泛化能力。实验结果表明，在自制图像缺陷数据集和NEU-DET（NEU surface defect database for Defect Detection Task）上，所提损失的尾部类平均精度均值（mAP）优于二分类交叉熵损失（BCE Loss），分别提高了32.02和7.40个百分点；与EQL v2（EQualization Loss v2）相比，分别提高了2.20和0.82个百分点，验证了所提损失能有效提升网络对尾部类的检测性能。

关键词: 长尾数据集, 累计梯度比值, 加权损失, 图像缺陷检测, 卷积神经网络

CLC Number:

TP391

Wei LI, Sixin LIANG, Jianzhou ZHANG. Long-tailed image defect detection based on gradient-guide weighted-deferred negative gradient decay loss[J]. Journal of Computer Applications, 2023, 43(10): 3267-3274.

李巍, 梁斯昕, 张建州. 基于梯度引导加权‒延迟负梯度衰减损失的长尾图像缺陷检测[J]. 《计算机应用》唯一官方网站, 2023, 43(10): 3267-3274.

Figures/Tables 14

Fig. 1 Basic network structure of Faster-RCNN

Fig. 2 Category prediction network based on GGW-DND Loss

Fig. 3 Samples of image defect detection dataset

Tab. 1 Comparison of detection results of different defect detection networks

模型	骨干网络	损失函数	mAP	mAP_f	mAP_r
Faster R-CNN	ResNet-50	CE Loss	70.05	92.04	48.06
Faster R-CNN	ResNet-50	BCE Loss	68.31	92.60	44.02
SSD	VGG16	CE Loss	64.61	86.68	42.54
YOLOv5	CSP-DarkNet	BCE Loss	49.96	80.67	19.24

Tab. 2 Comparison of detection results based on different loss functions

损失函数	mAP	mAP_f	mAP_r
CE Loss	70.05	92.04	48.06
BCE Loss	68.31	92.60	44.02
Focal Loss	54.05	87.28	20.82
Logit Adjustment	72.12	86.72	57.52
EQL	73.92	91.97	55.87
BAGS	82.49	91.62	73.35
EQL v2	83.06	92.28	73.84
GGW-DND Loss	84.09	92.13	76.04

Fig. 4 Mapping functions under different combinations of β and γ

Tab. 3 Detection results under different combinations of β and γ

$β$	$γ$	mAP/%	mAP_f/%	mAP_r/%
6	0.6	82.96	92.20	73.71
	0.7	82.60	90.83	74.37
	0.8	83.73	91.72	75.73
8	0.6	82.09	91.79	72.39
	0.7	83.69	92.12	75.26
	0.8	80.70	90.73	70.67
10	0.6	83.07	91.90	74.25
	0.7	81.92	91.41	72.43
	0.8	76.62	89.68	63.56
12	0.6	80.48	91.83	69.12
	0.7	80.46	91.19	69.73
	0.8	75.10	89.14	61.07

Tab. 3 Detection results under different combinations of β and γ

$β$	$γ$	mAP/%	mAP_f/%	mAP_r/%
6	0.6	82.96	92.20	73.71
	0.7	82.60	90.83	74.37
	0.8	83.73	91.72	75.73
8	0.6	82.09	91.79	72.39
	0.7	83.69	92.12	75.26
	0.8	80.70	90.73	70.67
10	0.6	83.07	91.90	74.25
	0.7	81.92	91.41	72.43
	0.8	76.62	89.68	63.56
12	0.6	80.48	91.83	69.12
	0.7	80.46	91.19	69.73
	0.8	75.10	89.14	61.07

Tab. 4 Detection results at different α

$α$	mAP/%	mAP_f/%	mAP_r/%
1	77.75	91.29	64.22
2	79.37	91.67	67.06
4	81.54	91.80	71.29
8	83.69	92.12	75.26
12	82.38	91.49	73.27

Tab. 4 Detection results at different α

$α$	mAP/%	mAP_f/%	mAP_r/%
1	77.75	91.29	64.22
2	79.37	91.67	67.06
4	81.54	91.80	71.29
8	83.69	92.12	75.26
12	82.38	91.49	73.27

Fig. 5 Corresponding negative gradient after scaling at different λ

Tab. 5 Detection results at different λ

$λ$	mAP/%	mAP_f/%	mAP_r/%
1	83.06	92.28	73.84
2	83.68	92.38	74.98
4	83.78	92.16	75.39
6	84.09	92.13	76.04
8	83.69	92.12	75.26
12	83.99	92.57	75.42

Tab. 5 Detection results at different λ

$λ$	mAP/%	mAP_f/%	mAP_r/%
1	83.06	92.28	73.84
2	83.68	92.38	74.98
4	83.78	92.16	75.39
6	84.09	92.13	76.04
8	83.69	92.12	75.26
12	83.99	92.57	75.42

Tab. 6 Cumulative gradient ratios of classifier nodes based on BCE Loss and GGW-DND Loss

损失函数	马赛克	偏色	亮度异常	高斯噪声	椒盐噪声	坏块
BCE Loss	0.73	0.65	0.67	0.13	0.10	0.18
GGW-DND Loss	1.22	1.16	1.16	0.84	0.84	0.98

Tab. 7 Results of ablation experiment

GGW Loss	DND Loss	mAP	mAP_f	mAP_r
		68.31	92.60	44.02
√		83.06	92.28	73.84
√	√	84.09	92.13	76.04

Tab. 8 NEU-DET dataset

缺陷类型	训练集样本数	测试集样本数
银纹	482	205
夹杂物	630	287
斑块	570	270
麻点表面	4	128
轧入氧化皮	3	185
划痕	3	155

Tab. 9 Comparison of detection results based on different loss functions on NEU-DET dataset

损失函数	mAP	mAP_f	mAP_r
BCE Loss^［33］	32.38	64.76	0.01
Focal Loss^［38］	33.45	66.90	0.00
Logit Adjustment^［54］	34.08	66.24	1.93
EQL^［55］	36.48	69.76	3.20
BAGS^［40］	34.89	66.03	3.75
EQL v2^［47］	36.71	66.83	6.59
GGW-DND Loss	36.85	66.29	7.41

References 55

1	王志明. 无参考图像质量评价综述［J］. 自动化学报， 2015， 41（6）：1062-1079. 10.16383/j.aas.2015.c140404
	WANG Z M. Review of no-reference image quality assessment［J］. Acta Automatica Sinica， 2015， 41（6）： 1062-1079. 10.16383/j.aas.2015.c140404
2	房亚男. 数字视频图像质量检测关键技术研究［J］. 科技创新导报， 2013（16）： 22-26. 10.3969/j.issn.1674-098X.2013.16.016
	FANG Y N. Research on key technologies of digital video image quality detection［J］. Science and Technology Innovation Herald， 2013（16）： 22-26. 10.3969/j.issn.1674-098X.2013.16.016
3	WANG F L， ZUO B. Detection of surface cutting defect on MagNet using Fourier image reconstruction［J］. Journal of Central South University， 2016， 23（5）： 1123-1131. 10.1007/s11771-016-0362-y
4	WEE C Y， PARAMESRAN R. Image sharpness measure using eigenvalues［C］// Proceedings of the 9th International Conference on Signal Processing. Piscataway： IEEE， 2008： 840-843. 10.1109/icosp.2008.4697259
5	HOU Z， PARKER J M. Texture defect detection using support vector machines with adaptive Gabor wavelet features［C］// Proceedings of the 7th IEEE Workshops on Applications of Computer Vision. Piscataway： IEEE， 2005： 275-280. 10.1109/acvmot.2005.115
6	CHA Y J， CHOI W， SUH G， et al. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types［J］. Computer-Aided Civil and Infrastructure Engineering， 2018， 33（9）： 731-747. 10.1111/mice.12334
7	TAO X， ZHANG D P， WANG Z H， et al. Detection of power line insulator defects using aerial images analyzed with convolutional neural networks［J］. IEEE Transactions on Systems， Man， and Cybernetics： Systems， 2020， 50（4）： 1486-1498. 10.1109/tsmc.2018.2871750
8	HE Y， SONG K C， MENG Q G， et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features［J］. IEEE Transactions on Instrumentation and Measurement， 2020， 69（4）： 1493-1504. 10.1109/tim.2019.2915404
9	陶显，侯伟，徐德. 基于深度学习的表面缺陷检测方法综述［J］. 自动化学报， 2021， 47（5）：1017-1034.
	TAO X， HOU W， XU D. A survey of surface defect detection methods based on deep learning［J］. Acta Automatica Sinica， 2021， 47（5）： 1017-1034.
10	LI J Y， SU Z F， GENG J H， et al. Real-time detection of steel strip surface defects based on improved YOLO detection network［J］. IFAC-PapersOnLine， 2018， 51（21）： 76-81. 10.1016/j.ifacol.2018.09.412
11	ZHANG C B， CHANG C C， JAMSHIDI M. Concrete bridge surface damage detection using a single-stage detector［J］. Computer-Aided Civil and Infrastructure Engineering， 2020， 35（4）： 389-409. 10.1111/mice.12500
12	CHEN S H， TSAI C C. SMD LED chips defect detection using a YOLOv3-dense model［J］. Advanced Engineering Informatics， 2021， 47： No.101255. 10.1016/j.aei.2021.101255
13	RUFF L， VANDERMEULEN R A， GÖRNITZ N， et al. Deep one-class classification［C］// Proceedings of the 35th International Conference on Machine Learning. New York： JMLR.org， 2018： 4393-4402.
14	BERGMANN P， FAUSER M， SATTLEGGER D， et al. Uninformed students： student-teacher anomaly detection with discriminative latent embeddings［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 4182-4191. 10.1109/cvpr42600.2020.00424
15	LIZNERSKI P， RUFF L， VANDERMEULEN R A， et al. Explainable deep one-class classification［EB/OL］. （2021-03-18）［2022-09-09］..
16	GOLAN I， EL-YANIV R. Deep anomaly detection using geometric transformations［C］// Proceedings of the 32nd International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2018： 9781-9791.
17	HASELMANN M， GRUBER D P， TABATABAI P. Anomaly detection using deep learning based image completion［C］// Proceedings of the 17th IEEE International Conference on Machine Learning and Applications. Piscataway： IEEE， 2018： 1237-1242. 10.1109/icmla.2018.00201
18	SAKURADA M， YAIRI T. Anomaly detection using autoencoders with nonlinear dimensionality reduction［C］// Proceedings of the MLSDA 2nd Workshop on Machine Learning for Sensory Data Analysis. New York： ACM， 2014： 4-11. 10.1145/2689746.2689747
19	ZHOU C， PAFFENROTH R C. Anomaly detection with robust deep autoencoders［C］// Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York： ACM， 2017： 665-674. 10.1145/3097983.3098052
20	GUPTA K， BHAVSAR A， SAO A K. Detecting mitotic cells in HEp-2 images as anomalies via one class classifier［J］. Computers in Biology and Medicine， 2019， 111： No.103328. 10.1016/j.compbiomed.2019.103328
21	NAPOLETANO P， PICCOLI F， SCHETTINI R. Anomaly detection in nanofibrous materials by CNN-based self-similarity［J］. Sensors， 2018， 18（1）： No.209. 10.3390/s18010209
22	CANDÈS E J， LI X D， MA Y， et al. Robust principal component analysis？［J］. Journal of the ACM， 2011， 58（3）： No.11. 10.1145/1970392.1970395
23	GONG D， LIU L Q， LE V， et al. Memorizing normality to detect anomaly： memory-augmented deep autoencoder for unsupervised anomaly detection［C］// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway： IEEE， 2019： 1705-1714. 10.1109/iccv.2019.00179
24	SCHLEGL T， SEEBÖCK P， WALDSTEIN S M， et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery［C］// Proceedings of the 2017 International Conference on Information Processing in Medical Imaging， LNCS 10265. Cham： Springer， 2017： 146-157.
25	LIN T Y， MAIRE M， BELONGIE S， et al. Microsoft COCO： common objects in context［C］// Proceedings of the 2014 European Conference on Computer Vision， LNCS 8693. Cham： Springer， 2014： 740-755.
26	EVERINGHAM M， van GOOL L， WILLIAMS C K I， et al. The PASCAL Visual Object Classes （VOC） challenge［J］. International Journal of Computer Vision， 2010， 88（2）： 303-338. 10.1007/s11263-009-0275-4
27	BUDA M， MAKI A， MAZUROWSKI M A. A systematic study of the class imbalance problem in convolutional neural networks［J］. Neural Networks， 2018， 106： 249-259. 10.1016/j.neunet.2018.07.011
28	HE H B， GARCIA E A. Learning from imbalanced data［J］. IEEE Transactions on Knowledge and Data Engineering， 2009， 21（9）： 1263-1284. 10.1109/tkde.2008.239
29	OUYANG W L， WANG X G， ZHANG C， et al. Factors in finetuning deep model for object detection with long-tail distribution［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 864-873. 10.1109/cvpr.2016.100
30	BYRD J， LIPTON Z C. What is the effect of importance weighting in deep learning？［C］// Proceedings of the 36th International Conference on Machine Learning. New York： JMLR.org， 2019： 872-881.
31	SHEN L， LIN Z C， HUANG Q M. Relay backpropagation for effective learning of deep convolutional neural networks［C］// Proceedings of the 2016 European Conference on Computer Vision， LNCS 9911. Cham： Springer， 2016： 467-482.
32	CHAWLA N V， BOWYER K W， HALL L O， et al. SMOTE： synthetic minority over-sampling technique［J］. Journal of Artificial Intelligence Research， 2002， 16： 321-357. 10.1613/jair.953
33	CUI Y， JIA M L， LIN T Y， et al. Class-balanced loss based on effective number of samples［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2019： 9260-9269. 10.1109/cvpr.2019.00949
34	WANG Y X， RAMANAN D， HEBERT M. Learning to model the tail［C］// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2017： 7032-7042.
35	HUANG C， LI Y N， LOY C C， et al. Learning deep representation for imbalanced classification［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 5375-5384. 10.1109/cvpr.2016.580
36	ZHANG X， FANG Z Y， WEN Y D， et al. Range loss for deep face recognition with long-tailed training data［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 5419-5428. 10.1109/iccv.2017.578
37	CAO K D， WEI C， GAIDON A， et al. Learning imbalanced datasets with label-distribution-aware margin loss［C］// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook， NY： Curran Associates Inc.， 2019： 1567-1578.
38	LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 2999-3007. 10.1109/iccv.2017.324
39	LI B Y， LIU Y， WANG X G. Gradient harmonized single-stage detector［C］// Proceedings of the 33rd AAAI Conference on Artificial Intelligence. Palo Alto， CA： AAAI Press， 2019： 8577-8584. 10.1609/aaai.v33i01.33018577
40	LI Y， WANG T， KANG B Y， et al. Overcoming classifier imbalance for long-tail object detection with balanced group softmax［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 10988-10997. 10.1109/cvpr42600.2020.01100
41	YIN X， YU X， SOHN K， et al. Feature transfer learning for face recognition with under-represented data［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2019： 5697-5706. 10.1109/cvpr.2019.00585
42	LIU Z W， MIAO Z Q， ZHAN X H， et al. Large-scale long-tailed recognition in an open world［C］// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2019： 2532-2541. 10.1109/cvpr.2019.00264
43	CHU P， BIAN X， LIU S P， et al. Feature space augmentation for long-tailed data［C］// Proceedings of the 2020 European Conference on Computer Vision， LNCS 12374. Cham： Springer， 2020： 694-710.
44	REN S Q， HE K M， GIRSHICK R， et al. Faster R-CNN： towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2017， 39（6）： 1137-1149. 10.1109/tpami.2016.2577031
45	HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 770-778. 10.1109/cvpr.2016.90
46	HE K M， GKIOXARI G， DOLLÁR P， et al. Mask R-CNN［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision. Piscataway： IEEE， 2017： 2980-2988. 10.1109/iccv.2017.322
47	TAN J R， LU X， ZHANG G， et al. Equalization loss v2： a new gradient balance approach for long-tailed object detection［C］// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2021： 1685-1694. 10.1109/cvpr46437.2021.00173
48	HE Y， SONG K， MENG Q， et al. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features［J］. IEEE Transactions on Instrumentation and Measurement， 2019， 69（4）： 1493-1504. 10.1109/tim.2019.2915404
49	KINGMA D P， BA J L. Adam： a method for stochastic optimization［EB/OL］. （2017-01-30）［2022-07-13］..
50	LIU W， ANGUELOV D， ERHAN D， et al. SSD： single shot multiBox detector［C］// Proceedings of the 2016 European Conference on Computer Vision， LNCS 9905. Cham： Springer， 2016： 21-37.
51	KUZNETSOVA A， MALEVA T， SOLOVIEV V. YOLOv5 versus YOLOv3 for apple detection［M］// KRAVETS A G， BOLSHAKOV A A， SHCHERBAKOV M. Cyber-Physical Systems： Modelling and Intelligent Control， SSDC 338. Cham： Springer， 2021： 349-358. 10.1007/978-3-030-66077-2_28
52	SIMONYAN K， ZISSERMAN A. Very deep convolutional networks for large-scale image recognition［EB/OL］. （2015-04-10）［2022-07-24］..
53	REDMON J， FARHADI A. YOLOv3： an incremental improvement［EB/OL］. （2018-04-08）［2022-08-09］.. 10.1109/cvpr.2017.690
54	MENON A K， JAYASUMANA S， RAWAT A S， et al. Long-tail learning via logit adjustment［EB/OL］. （2021-07-09）［2022-05-22］..
55	TAN J R， WANG C B， LI B Y， et al. Equalization loss for long-tailed object recognition［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 11659-11668. 10.1109/cvpr42600.2020.01168

[1]	Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910.
[2]	Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499.
[3]	Yangyi GAO, Tao LEI, Xiaogang DU, Suiyong LI, Yingbo WANG, Chongdan MIN. Crowd counting and locating method based on pixel distance map and four-dimensional dynamic convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2233-2242.
[4]	Dongwei WANG, Baichen LIU, Zhi HAN, Yanmei WANG, Yandong TANG. Deep network compression method based on low-rank decomposition and vector quantization [J]. Journal of Computer Applications, 2024, 44(7): 1987-1994.
[5]	Mengyuan HUANG, Kan CHANG, Mingyang LING, Xinjie WEI, Tuanfa QIN. Progressive enhancement algorithm for low-light images based on layer guidance [J]. Journal of Computer Applications, 2024, 44(6): 1911-1919.
[6]	Jianjing LI, Guanfeng LI, Feizhou QIN, Weijun LI. Multi-relation approximate reasoning model based on uncertain knowledge graph embedding [J]. Journal of Computer Applications, 2024, 44(6): 1751-1759.
[7]	Min SUN, Qian CHENG, Xining DING. CBAM-CGRU-SVM based malware detection method for Android [J]. Journal of Computer Applications, 2024, 44(5): 1539-1545.
[8]	Wenshuo GAO, Xiaoyun CHEN. Point cloud classification network based on node structure [J]. Journal of Computer Applications, 2024, 44(5): 1471-1478.
[9]	Tianhua CHEN, Jiaxuan ZHU, Jie YIN. Bird recognition algorithm based on attention mechanism [J]. Journal of Computer Applications, 2024, 44(4): 1114-1120.
[10]	Lijun XU, Hui LI, Zuyang LIU, Kansong CHEN, Weixuan MA. 3D-GA-Unet： MRI image segmentation algorithm for glioma based on 3D-Ghost CNN [J]. Journal of Computer Applications, 2024, 44(4): 1294-1302.
[11]	Jie WANG, Hua MENG. Image classification algorithm based on overall topological structure of point cloud [J]. Journal of Computer Applications, 2024, 44(4): 1107-1113.
[12]	Yongfeng DONG, Jiaming BAI, Liqin WANG, Xu WANG. Chinese named entity recognition combining prior knowledge and glyph features [J]. Journal of Computer Applications, 2024, 44(3): 702-708.
[13]	Ruifeng HOU, Pengcheng ZHANG, Liyuan ZHANG, Zhiguo GUI, Yi LIU, Haowen ZHANG, Shubin WANG. Iterative denoising network based on total variation regular term expansion [J]. Journal of Computer Applications, 2024, 44(3): 916-921.
[14]	Jingxian ZHOU, Xina LI. UAV detection and recognition based on improved convolutional neural network and radio frequency fingerprint [J]. Journal of Computer Applications, 2024, 44(3): 876-882.
[15]	Jiawei ZHANG, Guandong GAO, Ke XIAO, Shengzun SONG. Violent crime hierarchy algorithm by joint modeling of improved hierarchical attention network and TextCNN [J]. Journal of Computer Applications, 2024, 44(2): 403-410.

Long-tailed image defect detection based on gradient-guide weighted-deferred negative gradient decay loss

基于梯度引导加权‒延迟负梯度衰减损失的长尾图像缺陷检测

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 55

Related Articles 15

Recommended Articles

Metrics