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

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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

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