LE–MSFE–DDNet: a defect detection network based on low-light enhancement and multi-scale feature extraction

Author:

Hu Weihua,Wang Tao,Wang Yangsai,Chen ZiYang,Huang Guoheng

Funder

the R & D projects in key areas of Guangdong Province

the National Natural Science Foundation of Guangdong Joint Funds

the Natural Science Foundation of Guangdong Province

the projects of science and technology plan of Guangdong Province

the Guangdong Provincial Key Laboratory of Cyber-Physical System

Publisher

Springer Science and Business Media LLC

Subject

Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Software

Cited by 23 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. PCB Defect Detection Model Based on Intrinsic Feature Decomposition and Multilevel Fusion Against Image Uncertainty;IEEE Sensors Journal;2024-06-15

2. Low-Light Image Restoration Using a Convolutional Neural Network;Journal of Electronic Materials;2024-05-08

3. A systematic review of deep learning approaches for surface defect detection in industrial applications;Engineering Applications of Artificial Intelligence;2024-04

4. MaCo: efficient unsupervised low-light image enhancement via illumination-based magnitude control;The Visual Computer;2024-02-09

5. Image Recognition in Low-Light Conditions with Deep Learning Model;2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT);2024-01-04

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