SDENet：基于多尺度注意力质量感知的合成缺陷数据评价网络

doi:10.11996/JG.j.2095-302X.2025010094

图学学报 ›› 2025, Vol. 46 ›› Issue (1): 94-103.DOI: 10.11996/JG.j.2095-302X.2025010094

• 图像处理与计算机视觉 • 上一篇下一篇

SDENet：基于多尺度注意力质量感知的合成缺陷数据评价网络

卢洋¹^,²^,³(), 陈林慧¹, 姜晓恒¹^,²^,³, 徐明亮¹^,²^,³()

1.郑州大学计算机与人工智能学院，河南郑州 450001
2.智能集群系统教育部工程研究中心，河南郑州 450001
3.国家超级计算郑州中心，河南郑州 450001

收稿日期:2024-07-04 接受日期:2024-09-23 出版日期:2025-02-28 发布日期:2025-02-14
通讯作者:徐明亮(1981-)，男，教授，博士。主要研究方向为人工智能、大数据、机器人和工业软件等。E-mail：iexumingliang@zzu.edu.cn
第一作者:卢洋(1991-)，女，讲师，博士。主要研究方向为人工智能、深度学习、计算机视觉和数字图像处理。E-mail：ieylu@zzu.edu.cn
基金资助:
国家自然科学基金青年科学基金(62102370);国家自然科学基金青年科学基金(62172371);国家自然科学基金青年科学基金(U21B2037);国家自然科学基金青年科学基金(U22B2051);中国博士后科学基金面上项目(2021M692917);国家重点研发计划课题(2021YFB3301504)

SDENet: a synthetic defect data evaluation network based on multi-scale attention quality perception

LU Yang¹^,²^,³(), CHEN Linhui¹, JIANG Xiaoheng¹^,²^,³, XU Mingliang¹^,²^,³()

1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou Henan 450001, China
2. Engineering Research Center of Intelligent Swarm Systems, Ministry of Education, Zhengzhou Henan 450001, China
3. National Supercomputing Center in Zhengzhou, Zhengzhou Henan 450001, China

Received:2024-07-04 Accepted:2024-09-23 Published:2025-02-28 Online:2025-02-14
Contact: XU Mingliang (1981-), professor, Ph.D. His main research interests cover artificial intelligence, big data, robotics and industrial software, etc. E-mail：iexumingliang@zzu.edu.cn
First author：LU Yang (1991-), lecturer, Ph.D. Her main research interests cover artificial intelligence, deep learning, computer vision and digital image processing. E-mail：ieylu@zzu.edu.cn
Supported by:
National Natural Science Foundation of China(62102370);National Natural Science Foundation of China(62172371);National Natural Science Foundation of China(U21B2037);National Natural Science Foundation of China(U22B2051);China Postdoctoral Science Foundation(2021M692917);National Key Research and Development Program of China(2021YFB3301504)

摘要/Abstract

摘要：

通过对数据扩增方式合成的缺陷数据进行质量评估，有助于实现缺陷数据高质量扩充，进而缓解缺陷数据不足导致的检测模型性能不佳问题。针对现有质量评价算法在评估合成缺陷数据质量时更关注数据的失真特性而忽略了对数据缺陷属性考量的问题，提出一种基于注意力特征增强(AFE)和多尺度注意力质量感知(MAQP)的模型SDENet，综合考虑数据的失真特性和缺陷属性进行质量评价。首先，AFE通过双分支池化操作提高模型对不同尺寸、位置缺陷的泛化能力，并结合注意力机制增强模型对特征的表达。其次，MAQP对AFE增强后的特征进行向量化与融合处理，以更好地感知合成缺陷数据质量。最后，对融合后的特征进行质量评估，得到最终的评估分数。在构建的合成道路裂缝缺陷数据集上进行实验，结果表明，SDENet模型在RMSE，RMAE，PLCC和SROCC指标上均取得最优结果，比次优模型依次提升10.7%，5.0%，1.8%和1.8%，验证了模型的有效性。在失真数据集TID2013上，SDENet模型也取得较有竞争的结果，在PLCC和SROCC指标上依次达到0.902和0.876。

关键词: 注意力机制, 特征增强, 特征融合, 合成缺陷数据, 质量评价

Abstract:

The quality evaluation of defect data synthesized through data augmentation can facilitate high-quality expansion of defect data, thereby mitigating the problem of poor detection model performance caused by insufficient defect data. When evaluating the quality of synthetic defect data, existing quality evaluation algorithms primarily focus on the distortion characteristics of the data but tend to overlook the defect attributes of the data. To address this issue, a SDENet model based on attention feature enhancement (AFE) and multi-scale attention quality perception (MAQP) was proposed, which comprehensively considered the distortion characteristics and defect attributes of synthesized defect data for quality evaluation. Firstly, the AFE module improved the model's generalization ability to defects of different sizes and positions through dual-branch pooling operation, while also using an attention mechanism to enhance the feature expression ability of the model. Secondly, the MAQP module vectorized and fused the features enhanced by AFE to better perceive the quality of synthetic defect data. Finally, the fused features were fed into the quality evaluation section, and the final evaluation score was generated. Experiments conducted on the constructed synthetic defect data set of road cracks demonstrated that the SDENet model achieved optimal results in RMSE, RMAE, PLCC, and SROCC metrics, with improvements of 10.7%, 5.0%, 1.8% and 1.8% compared to the suboptimal model, thereby verifying the effectiveness of the model. On the distorted dataset TID2013, the SDENet model also produced competitive results, reaching 0.902 and 0.876 on the PLCC and SROCC metrics, respectively.

Key words: attention mechanism, feature enhancement, feature fusion, synthetic defect data, quality evaluation

中图分类号:

卢洋, 陈林慧, 姜晓恒, 徐明亮. SDENet：基于多尺度注意力质量感知的合成缺陷数据评价网络[J]. 图学学报, 2025, 46(1): 94-103.

LU Yang, CHEN Linhui, JIANG Xiaoheng, XU Mingliang. SDENet: a synthetic defect data evaluation network based on multi-scale attention quality perception[J]. Journal of Graphics, 2025, 46(1): 94-103.

图/表 9

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模型	Param/M	Flops/G	Speed/FPS	合成缺陷数据集				TID2013数据集
模型	Param/M	Flops/G	Speed/FPS	RMSE↓	RMAE↓	PLCC↑	SROCC↑	PLCC↑	SROCC↑
TANet^[44]	13.88	2.16	125.6	1.195	0.992	0.786	0.786	0.510	0.502
MobileNetV2^[40]	2.23	0.33	225.6	1.170	0.970	0.807	0.805	0.665	0.548
MAMIQA^[45]	39.22	5.45	68.4	0.975	0.880	0.865	0.869	0.937	0.928
TReSNet^[43]	34.46	8.39	43.3	0.944	0.858	0.878	0.881	0.883	0.863
DBCNN^[42]	15.31	16.50	175.1	0.808	0.802	0.886	0.888	0.865	0.816
ResNet18^[21]	11.44	1.82	380.0	0.768	0.776	0.904	0.904	0.843	0.810
HyperIQA^[41]	27.38	4.34	171.1	0.774	0.767	0.904	0.905	0.858	0.840
SDENet(本文)	11.46	1.83	351.5	0.667	0.717	0.922	0.923	0.902	0.876

模型	Param/M	Flops/G	Speed/FPS	合成缺陷数据集				TID2013数据集
模型	Param/M	Flops/G	Speed/FPS	RMSE↓	RMAE↓	PLCC↑	SROCC↑	PLCC↑	SROCC↑
TANet^[44]	13.88	2.16	125.6	1.195	0.992	0.786	0.786	0.510	0.502
MobileNetV2^[40]	2.23	0.33	225.6	1.170	0.970	0.807	0.805	0.665	0.548
MAMIQA^[45]	39.22	5.45	68.4	0.975	0.880	0.865	0.869	0.937	0.928
TReSNet^[43]	34.46	8.39	43.3	0.944	0.858	0.878	0.881	0.883	0.863
DBCNN^[42]	15.31	16.50	175.1	0.808	0.802	0.886	0.888	0.865	0.816
ResNet18^[21]	11.44	1.82	380.0	0.768	0.776	0.904	0.904	0.843	0.810
HyperIQA^[41]	27.38	4.34	171.1	0.774	0.767	0.904	0.905	0.858	0.840
SDENet(本文)	11.46	1.83	351.5	0.667	0.717	0.922	0.923	0.902	0.876

方法	RMSE↓	RMAE↓	PLCC↑	SROCC↑
Baseline	0.768	0.776	0.904	0.904
Baseline+MQP	0.730	0.747	0.909	0.911
Baseline+MAQP	0.667	0.717	0.922	0.923

方法	RMSE↓	RMAE↓	PLCC↑	SROCC↑
Baseline	0.768	0.776	0.904	0.904
Baseline+MQP	0.730	0.747	0.909	0.911
Baseline+MAQP	0.667	0.717	0.922	0.923

操作	AFE				RMSE↓	RMAE↓	PLCC↑	SROCC↑
操作	L₁	L₂	L₃	L₄	RMSE↓	RMAE↓	PLCC↑	SROCC↑
NULL-NULL-NULL-NULL	-	-	-	-	0.730	0.747	0.909	0.911
NULL-A-NULL-NULL	-	√	-	-	0.716	0.743	0.914	0.914
NULL-A-A-NULL	-	√	√	-	0.691	0.726	0.918	0.918
NULL-A-A-A(本文)	-	√	√	√	0.667	0.717	0.922	0.923
A-A-A-A	√	√	√	√	0.676	0.717	0.919	0.919

SDENet：基于多尺度注意力质量感知的合成缺陷数据评价网络

SDENet: a synthetic defect data evaluation network based on multi-scale attention quality perception

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图/表 9

参考文献 46

相关文章 15

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Metrics

本文评价

注意力模块	RMSE↓	RMAE↓	PLCC↑	SROCC↑
-	0.734	0.742	0.907	0.912
SE	0.701	0.730	0.912	0.912
CBAM	0.699	0.733	0.915	0.917
ECA	0.692	0.731	0.917	0.918
本文	0.667	0.717	0.922	0.923

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模型	对比度失真数据				高斯噪声失真数据				椒盐噪声失真数据
模型	RMSE↓	RMAE↓	PLCC↑	SROCC↑	RMSE↓	RMAE↓	PLCC↑	SROCC↑	RMSE↓	RMAE↓	PLCC↑	SROCC↑
TANet^[44]	1.574	1.157	0.887	0.895	1.149	0.970	0.826	0.830	1.566	1.148	0.674	0.671
MobileNetV2^[40]	1.139	0.954	0.880	0.888	0.997	0.897	0.781	0.763	0.936	0.875	0.829	0.827
MAMIQA^[45]	0.947	0.852	0.904	0.904	0.935	0.860	0.838	0.830	0.824	0.790	0.875	0.871
TReSNet^[43]	0.934	0.833	0.925	0.925	0.651	0.717	0.906	0.904	0.736	0.770	0.911	0.911
DBCNN^[42]	0.768	0.773	0.934	0.934	0.684	0.740	0.889	0.884	0.767	0.775	0.887	0.882
ResNet18^[21]	0.780	0.802	0.935	0.932	0.676	0.727	0.920	0.913	0.638	0.713	0.926	0.926
HyperIQA^[41]	0.824	0.806	0.927	0.933	0.651	0.712	0.901	0.903	0.766	0.779	0.904	0.897
SDENet (本文)	0.723	0.770	0.943	0.944	0.608	0.690	0.921	0.915	0.590	0.689	0.934	0.933

模型	对比度失真数据				高斯噪声失真数据				椒盐噪声失真数据
模型	RMSE↓	RMAE↓	PLCC↑	SROCC↑	RMSE↓	RMAE↓	PLCC↑	SROCC↑	RMSE↓	RMAE↓	PLCC↑	SROCC↑
TANet^[44]	1.574	1.157	0.887	0.895	1.149	0.970	0.826	0.830	1.566	1.148	0.674	0.671
MobileNetV2^[40]	1.139	0.954	0.880	0.888	0.997	0.897	0.781	0.763	0.936	0.875	0.829	0.827
MAMIQA^[45]	0.947	0.852	0.904	0.904	0.935	0.860	0.838	0.830	0.824	0.790	0.875	0.871
TReSNet^[43]	0.934	0.833	0.925	0.925	0.651	0.717	0.906	0.904	0.736	0.770	0.911	0.911
DBCNN^[42]	0.768	0.773	0.934	0.934	0.684	0.740	0.889	0.884	0.767	0.775	0.887	0.882
ResNet18^[21]	0.780	0.802	0.935	0.932	0.676	0.727	0.920	0.913	0.638	0.713	0.926	0.926
HyperIQA^[41]	0.824	0.806	0.927	0.933	0.651	0.712	0.901	0.903	0.766	0.779	0.904	0.897
SDENet (本文)	0.723	0.770	0.943	0.944	0.608	0.690	0.921	0.915	0.590	0.689	0.934	0.933

模型	低噪声数据				高噪声数据
模型	RMSE↓	RMAE↓	PLCC↑	SROCC↑	RMSE↓	RMAE↓	PLCC↑	SROCC↑
TANet^[44]	1.092	0.944	0.866	0.867	0.955	0.872	0.890	0.889
MobileNetV2^[40]	0.986	0.897	0.835	0.836	0.957	0.870	0.867	0.878
MAMIQA^[45]	0.918	0.839	0.873	0.876	0.816	0.803	0.914	0.914
TReSNet^[43]	0.687	0.710	0.925	0.921	0.833	0.797	0.908	0.915
DBCNN^[42]	0.746	0.765	0.896	0.889	0.761	0.775	0.917	0.917
ResNet18^[21]	0.696	0.753	0.927	0.918	0.790	0.784	0.913	0.916
HyperIQA^[41]	0.725	0.757	0.919	0.914	0.699	0.744	0.939	0.939
SDENet (本文)	0.621	0.707	0.929	0.917	0.637	0.665	0.946	0.944

模型	低噪声数据				高噪声数据
模型	RMSE↓	RMAE↓	PLCC↑	SROCC↑	RMSE↓	RMAE↓	PLCC↑	SROCC↑
TANet^[44]	1.092	0.944	0.866	0.867	0.955	0.872	0.890	0.889
MobileNetV2^[40]	0.986	0.897	0.835	0.836	0.957	0.870	0.867	0.878
MAMIQA^[45]	0.918	0.839	0.873	0.876	0.816	0.803	0.914	0.914
TReSNet^[43]	0.687	0.710	0.925	0.921	0.833	0.797	0.908	0.915
DBCNN^[42]	0.746	0.765	0.896	0.889	0.761	0.775	0.917	0.917
ResNet18^[21]	0.696	0.753	0.927	0.918	0.790	0.784	0.913	0.916
HyperIQA^[41]	0.725	0.757	0.919	0.914	0.699	0.744	0.939	0.939
SDENet (本文)	0.621	0.707	0.929	0.917	0.637	0.665	0.946	0.944