Computer Science ›› 2021, Vol. 48 ›› Issue (7): 213-218.doi: 10.11896/jsjkx.200800127

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Low Light Image Fusion Detection Method Based on Lego Filter and SSD

LI Lin1, LIU Xue-liang1, ZHAO Ye1, JI Ping2   

  1. 1 School of Computer and Information Engineering,Hefei University of Technology,Hefei 230601,China
    2 Department of Electronic Information and Electrical Engineering,Hefei University,Hefei 230601,China
  • Received:2020-08-19 Revised:2020-10-02 Online:2021-07-15 Published:2021-07-02
  • About author:LI Lin,born in 1994,postgraduate.Her main research interests include object detection and computer vision.(804082803@qq.com)
    LIU Xue-liang,born in 1981,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include multimedia information retrieval and so on.
  • Supported by:
    Key R&D plan of the Ministry of Science and Technology(2018AAA0102002),National Natural Science Foundation of China(61976076,61632007,61932009,61806066) and Natural Science Research Projects of Universities in Anhui Province(KJ2018A0545).

Abstract: Aiming at the problem that target detection is prone to misdetection or missing detection due to the complex background environment of low light image,this paper proposes a method to improve the accuracy and speed of low light image based on SSD object detection.Firstly,the low light image is enhanced,the processed enhanced image and the original low light image are respectively input into the SSD network structure with Lego filter for training detection.The two detection models are used to train and detect the enhanced data set,and a series of candidate frames are obtained.Finally,the non-repeated frames in the candidate frames are fused to mark the target in the correct position,so as to improve the detection accuracy of low light image.At the same time,Lego filter is integrated into the network structure to reduce the model parameters of network training,so as to improve the detection speed.Experimental results show that,when Lego filter is integrated in different positions of the network structure,the parameters of the model reduce by 8.9% and 29.5%,and the numbers of floating-point operations reduce by 6.8% and 34.9%.After fusion processing,the detection accuracy improves by 3% ~7%.This method is more suitable for practical application,effectively improves the detection speed and accuracy of low light image,and expands the application range of object detection.

Key words: Fusio, Lego filter, Low light image, Object detection, Single Shot MultiBox Detector algorithm

CLC Number: 

  • TP391
[1]ANDREOPOULOS A,TSOTSOS J K.50 years of object recognition:Directions forward[J].Computer Vision and Image Understanding,2013,117(8):827-891.
[2]LORE K G,AKINTAYO A,SARKAR S.LLnet:a deep autoencoder approach to natural low-light image enhancement[J].Pattern Recognition,2017,61:650-662.
[3]WEI C,WANG W,YANG W,et al.Deep retinex decomposition for low-light enhancement[J].arXiv:1808.04560, 2018.
[4]ZHANG Y H,ZHANG J W,GUO X J.Kindling the Darkness:A Practical Low-light Image Enhancer[C]//Proceedings of the 27th ACM International Conference on Multimedia.2019:1632-1640.
[5]ZEILER M D,FERGUS R.Visualizing and Understanding Convolutional Networks[C]//European Conference on Computer Vision.Springer,2014:818-833.
[6]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems.2012:1097-1105.
[7]REDMON J,DIVVALA S,GIRSHICK R.You Only LookOnce:Unified,Real Time Object Detection[C]//Computer Vision & Pattern Recognition.IEEE,2016:13.
[8]LIU W,DRAGOMIR A,DUMITRU E.SSD:single shot multibox detector[C]//Proc of Computer Vision and Pattern Recognition.2016:21-37.
[9]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587.
[10]EVERINGHAM M,GOOL L V,WILLIAMS C K I,et al.The Pascal Visual Object Classes (VOC) Challenge[J].International Journal of Computer Vision,2010,88(2):303-338.
[11]LIN T,MAIRE M,BELONGIE S,et al.Microsoft coco:Co-mmon objects in context[C]//European Conference on Compu-ter Vision.Cham:Springer,2014:740-755.
[12]ZOU Z X,SHI Z W,GUO Y H.Object Detection in 20 Years:A Survey[J].arXiv:1905.05055v2,2019.
[13]ZHANG Q,YUAN G,XIAO C,et al.High-quality exposurecorrection of underexposed photos[C]//Proceedings of the 26th ACM international conference on Multimedia.2018:582-590.
[14]RASMUS R,GUILLAUMIN M,GOOL L V.Non-maximumSuppression for Object Detection by Passing Messages Between Windows[C]//Asian Conference on Computer Vision.Cham:Springer,2014.
[15]BAI C,HUANG L,CHEN J N,et al.Optimization of deep con-volutional neural network for large scale image classification[J].Journal of Software,2018,29(4):1029-1038.
[16]LIN T Y,DOLLÁR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2117-2125.
[17]YANG Z,WANG Y,LIU C,et al.Legonet:Efficient convolu-tional neural networks with lego filters[C]//International Conference on Machine Learning.PMLR,2019:7005-7014.
[18]NAVANEETH B,BHARAT S,RAMA C,et al.Soft-nms-improving object detection with one line of code[C]//Proc of IEEE International Conference on Computer Vision.2017:5562-5570.
[19]LOH Y P,CHAN C S.Getting to know low-light images with the exclusively dark dataset[J].Computer Vision and Image Understanding.2019:30-42.
[20]FU X,ZENG D,HUANG Y,et al.A fusion-based enhancingmethod for weakly illuminated images[J].Signal Processing,2016,129:82-96.
[21]GUO X,LI Y,LING H.Lime:Low-light image enhancement via illumination map estimation[J].IEEE Trans on Image Proces-sing,2017,26:982-993.
[22]MITTAL A,SOUNDARARAJAN R,BOVIK A.Making a“Completely Blind” Image Quality Analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212.
[23]YUE G,HOU C,ZHOU T.Blind quality assessment of tone-mapped images considering colorfulness,naturalness,and structure[J].IEEE Transactions on Industrial Electronics,2019,66(5):3784-3793.
[24]ZHAI G,WU X,YANG X,et al.A Psychovisual Quality Metric in Free-Energy Principle[J].IEEE Transactions on Image Processing,2012,21(1):41-52.
[25]GU K,LIN W,ZHAI G,et al.No-reference quality metric of contrast-distorted images based on information maximization[J].IEEE Transactions on Cybernetics,2017,47(12):4559-4565.
[1] CAO Xiao-wen, LIANG Mei-yu, LU Kang-kang. Fine-grained Semantic Reasoning Based Cross-media Dual-way Adversarial Hashing Learning Model [J]. Computer Science, 2022, 49(9): 123-131.
[2] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[3] WU Zi-yi, LI Shao-mei, JIANG Meng-han, ZHANG Jian-peng. Ontology Alignment Method Based on Self-attention [J]. Computer Science, 2022, 49(9): 215-220.
[4] QIN Qi-qi, ZHANG Yue-qin, WANG Run-ze, ZHANG Ze-hua. Hierarchical Granulation Recommendation Method Based on Knowledge Graph [J]. Computer Science, 2022, 49(8): 64-69.
[5] WEI Kai-xuan, FU Ying. Re-parameterized Multi-scale Fusion Network for Efficient Extreme Low-light Raw Denoising [J]. Computer Science, 2022, 49(8): 120-126.
[6] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[7] WANG Can, LIU Yong-jian, XIE Qing, MA Yan-chun. Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization [J]. Computer Science, 2022, 49(8): 157-164.
[8] SHEN Xiang-pei, DING Yan-rui. Multi-detector Fusion-based Depth Correlation Filtering Video Multi-target Tracking Algorithm [J]. Computer Science, 2022, 49(8): 184-190.
[9] YAN Jia-dan, JIA Cai-yan. Text Classification Method Based on Information Fusion of Dual-graph Neural Network [J]. Computer Science, 2022, 49(8): 230-236.
[10] CHEN Ming-xin, ZHANG Jun-bo, LI Tian-rui. Survey on Attacks and Defenses in Federated Learning [J]. Computer Science, 2022, 49(7): 310-323.
[11] ZHANG Yuan, KANG Le, GONG Zhao-hui, ZHANG Zhi-hong. Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM [J]. Computer Science, 2022, 49(7): 31-39.
[12] ZHANG Ying-tao, ZHANG Jie, ZHANG Rui, ZHANG Wen-qiang. Photorealistic Style Transfer Guided by Global Information [J]. Computer Science, 2022, 49(7): 100-105.
[13] CHENG Cheng, JIANG Ai-lian. Real-time Semantic Segmentation Method Based on Multi-path Feature Extraction [J]. Computer Science, 2022, 49(7): 120-126.
[14] YU Shu-hao, ZHOU Hui, YE Chun-yang, WANG Tai-zheng. SDFA:Study on Ship Trajectory Clustering Method Based on Multi-feature Fusion [J]. Computer Science, 2022, 49(6A): 256-260.
[15] LAI Teng-fei, ZHOU Hai-yang, YU Fei-hong. Real-time Extend Depth of Field Algorithm for Video Processing [J]. Computer Science, 2022, 49(6A): 314-318.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!