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Night-time vehicle model recognition based on domain adaptation

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Abstract

Owing to the low brightness, low contrast, and high labeling difficulty of night-time vehicle images, night-time vehicle model recognition (NVMR) faces significant challenges. To address these challenges, this paper proposes the Night-time Vehicle Model Recognition (DA-NVMR) method based on domain adaptation theory and Generative Adversarial Networks (GANs). The proposed method realizes the NVMR based on the VMR model trained on daytime vehicle images. In DA-NVMR, a Domain Adaption Network (DANet) is designed to find the mapping between night-time vehicle images and daytime vehicle images. The DANet consists of two modules: the decomposition module and conversion module. The decomposition module decomposes the vehicle images into reflectance images and illumination images. The conversion module realizes the conversion between the illumination of the night-time and daytime vehicle images. Experiments based on the simulated night-time vehicle dataset and real night-time vehicle dataset confirmed that DA-NVMR can effectively identify night-time vehicle models. Compared with other low-light image enhancement methods and domain adaptation methods, the Top-1 recognition accuracy of the proposed method improved by at least 2% and 1%, respectively.

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References

  1. Abdullah-Al-Wadud M, Kabir MH, Dewan MAA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600

    Article  Google Scholar 

  2. Anoosheh A, Sattler T, Timofte R, Pollefeys M, Van Gool L (2019) Night-to-day image translation for retrieval-based localization. In: 2009 International Conference on Robotics and Automation (ICRA). IEEE, pp 5958–5964

  3. Borgwardt KM, Gretton A, Rasch MJ, Kriegel H-P, Schölkopf B, Smola AJ (2006) Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics 22(14):e49–e57

    Article  Google Scholar 

  4. Castellano G, Castiello C, Mencar C, Vessio G (2020) Crowd detection in aerial images using spatial graphs and fully-convolutional neural networks. IEEE Access 8:64534–64544

    Article  Google Scholar 

  5. Chen C, Chen Q, Do MN, Koltun V (2019) Seeing motion in the dark. In: Proceedings of the IEEE/CVF International conference on computer vision, pp 3185–3194

  6. Chen C, Chen Q, Xu J, Koltun V (2018) Learning to see in the dark. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 3291–3300

  7. Chen Wei WYJL, Wang Wenjing (2018) Deep retinex decomposition for low-light enhancement. In: British machine vision conference

  8. Fang J, Zhou Y, Yu Y, Du S (2017) Fine-grained vehicle model recognition using a coarse-to-fine convolutional neural network architecture. IEEE Trans Intell Transp Syst 18(7):1782–1792

    Article  Google Scholar 

  9. Frégier Y, Gouray J-B (2019) Mind2mind:, transfer learning for gans, arXiv:1906.11613

  10. Ganin Y, Ustinova E, Ajakan H, Germain P, Larochelle H, Laviolette F, Marchand M, Lempitsky V (2016) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

    MathSciNet  Google Scholar 

  11. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in neural information processing systems, vol 27

  12. He H, Shao Z, Tan J (2015) Recognition of car makes and models from a single traffic-camera image. IEEE Trans Intell Transport Syst 16(12):1–11

    Google Scholar 

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  14. Hines G, Rahman Z-U, Jobson D, Woodell G (2005) Single-scale retinex using digital signal processors. In: Global signal processing conference

  15. Hoffman J, Tzeng E, Park T, Zhu J-Y, Isola P, Saenko K, Efros A, Darrell T (2018) Cycada: Cycle-consistent adversarial domain adaptation. In: International conference on machine learning, PMLR, pp 1989–1998

  16. Jiang Y, Gong X, Liu D, Cheng Y, Fang C, Shen X, Yang J, Zhou P, Wang Z (2021) Enlightengan: Deep light enhancement without paired supervision. IEEE Trans Image Process 30:2340–2349

    Article  Google Scholar 

  17. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision, Springer, pp 694–711

  18. Juefei-Xu F, Boddeti VN, Savvides M (2017) Gang of gans:, Generative adversarial networks with maximum margin ranking, arXiv:1704.04865

  19. Ke X, Zhang Y (2020) Fine-grained vehicle type detection and recognition based on dense attention network. Neurocomputing 399:247–257

    Article  Google Scholar 

  20. Krause J, Gebru T, Deng J, Li L-J, Fei-Fei L (2014) Learning features and parts for fine-grained recognition. In: 2014 22nd International conference on pattern recognition, pp 26–33

  21. Krause J, Stark M, Deng J, Fei-Fei L (2013) 3d object representations for fine-grained categorization. In: Proceedings of the IEEE international conference on computer vision workshops, pp 554–561

  22. Land EH, McCann JJ (1971) Lightness and retinex theory. J Opt Soc Am 61(1):1–11

    Article  Google Scholar 

  23. Lee S, Kim D, Kim N, Jeong S-G (2019) Drop to adapt: Learning discriminative features for unsupervised domain adaptation. In: Proceedings of the IEEE/CVF International conference on computer vision, pp 91–100

  24. Li P, Ni Z, Zhu X, Song J (2020) Transfer learning with joint inter-class and inter-domain distributional adaptation. Pattern Recognit Artif Intell 33(1):1–10

    Article  Google Scholar 

  25. Li Y, Peng X (2019) Learning domain adaptive features with unlabeled domain bridges, arXiv:1912.05004

  26. Lin Y-L, Morariu VI, Hsu W, Davis LS (2014) Jointly optimizing 3d model fitting and fine-grained classification. In: European conference on computer vision, Springer, pp 466–480

  27. Llorca DF, Colás D, Daza IG, Parra I, Sotelo MA (2014) Vehicle model recognition using geometry and appearance of car emblems from rear view images. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp 3094–3099

  28. Long M, Cao Y, Wang J, Jordan M (2015) Learning transferable features with deep adaptation networks. In: International conference on machine learning, PMLR, pp 97–105

  29. Long M, Cao Z, Wang J, Jordan MI (2017) Conditional adversarial domain adaptation, arXiv:1705.10667

  30. Lore KG, Akintayo A, Sarkar S (2017) Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn 61:650–662

    Article  Google Scholar 

  31. Nadeem Z, Khan Z, Mir U, Mir UI, Khan S, Nadeem H, Sultan J (2022) Pakistani traffic-sign recognition using transfer learning. Multimed Tools Appl 81(6):8429–8449

    Article  Google Scholar 

  32. Pizer SM, Amburn EP, Austin JD, Cromartie R, Geselowitz A, Greer T, ter Haar Romeny B, Zimmerman JB, Zuiderveld K (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39 (3):355–368

    Article  Google Scholar 

  33. Pizer S, Johnston R, Ericksen J, Yankaskas B, Muller K (1990) Contrast-limited adaptive histogram equalization: speed and effectiveness. In: Proceedings of the 1st Conference on visualization in biomedical computing, pp 337–345

  34. Rahman CBMSS, Fookes Mohammad Mahfujur (2020) Correlation-aware adversarial domain adaptation and generalization. Pattern Recognit 100:107124

    Article  Google Scholar 

  35. Rahman MM, Fookes C, Baktashmotlagh M, Sridharan S (2019) Multi-component image translation for deep domain generalization. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, pp 579–588

  36. Rahman Z-U, Jobson DJ, Woodell GA (1996) Multi-scale retinex for color image enhancement. In: Proceedings of 3rd IEEE International conference on image processing, vol 3. IEEE, pp 1003–1006

  37. Rassem TH, Khoo BE (2015) Performance evaluation of new colour histogram-based interest point detectors. Multimed Tools Appl 74 (24):11357–11398

    Article  Google Scholar 

  38. Rodriguez AL, Mikolajczyk K (2019) Domain adaptation for object detection via style consistency, arXiv:1911.10033

  39. Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, pp 234–241

  40. Shen L, Yue Z, Feng F, Chen Q, Liu S, Ma J (2017) Msr-net: Low-light image enhancement using deep convolutional network. arXiv:1711.02488

  41. Shi Y, Wu X, Zhu M (2019) Low-light image enhancement algorithm based on retinex and generative adversarial network, arXiv:1906.06027

  42. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556

  43. Sochor J, Herout A, Havel J (2016) Boxcars: 3d boxes as cnn input for improved fine-grained vehicle recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3006–3015

  44. Sochor J, Špaňhel J, Herout A (2018) Boxcars: Improving fine-grained recognition of vehicles using 3-d bounding boxes in traffic surveillance. IEEE Trans Intell Transp Syst 20(1):97–108

    Article  Google Scholar 

  45. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1–9

  46. Thamizhvani T, Ahmed K, Hemalatha R, Dhivya A, Chandrasekaran R (2021) Enhancement of mri images of hamstring avulsion injury using histogram based techniques. Multimed Tools Appl 80(8):12117–12134

    Article  Google Scholar 

  47. WANG Y, PENG J, WANG H, WANG M (2022) Progressive learning with multi-scale attention network for cross-domain vehicle re-identification. Inf Sci 65:160103

    Google Scholar 

  48. Wang Y (2021) Survey on deep multi-modal data analytics: Collaboration, rivalry, and fusion. ACM Transactions on Multimedia Computing, and Communications, and Applications (TOMM) 17(1s):1–25

    Google Scholar 

  49. Wang Z, Jing B, Ni Y, Dong N, Xie P, Xing EP (2019) Adversarial domain adaptation being aware of class relationships, arXiv:1905.11931

  50. Wang P, Wang Z, Lv D, Zhang C, Wang Y (2021) Low illumination color image enhancement based on gabor filtering and retinex theory. Multimed Tools Appl 80(12):17705–17719

    Article  Google Scholar 

  51. Wang W, Wei C, Yang W, Liu J (2018) Gladnet: Low-light enhancement network with global awareness. In: 2018 13th IEEE International conference on automatic face & gesture recognition (FG 2018). IEEE, pp 751–755

  52. Xue H (2021) Low light image enhancement based on modified retinex optimized by fractional order gradient descent with momentum rbf neural network. Multimed Tools Appl 80(12):19057–19077

    Article  Google Scholar 

  53. Yan L, Fu J, Wang C, Ye Z, Chen H, Ling H (2021) Enhanced network optimized generative adversarial network for image enhancement. Multimed Tools Appl 80(9):14363–14381

    Article  Google Scholar 

  54. Yan K, Kou L, Zhang D (2017) Learning domain-invariant subspace using domain features and independence maximization. IEEE Trans Cybern 48 (1):288–299

    Article  Google Scholar 

  55. Yang L, Luo P, Change Loy C, Tang X (2015) A large-scale car dataset for fine-grained categorization and verification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3973–3981

  56. Ying Z, Li G, Ren Y, Wang R, Wang W (2017) A new image contrast enhancement algorithm using exposure fusion framework. In: International conference on computer analysis of images and patterns, Springer, pp 36–46

  57. Yu C, Wang J, Chen Y, Huang M (2019) Transfer learning with dynamic adversarial adaptation network. In: 2019 IEEE International Conference on Data Mining (ICDM). IEEE, pp 778–786

  58. Yu C, Wang J, Chen Y, Wu Z (2019) Accelerating deep unsupervised domain adaptation with transfer channel pruning. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1–8

  59. Zhang Y, Tang H, Jia K, Tan M (2019) Domain-symmetric networks for adversarial domain adaptation. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 5031–5040

  60. Zhang W, Wu D (2020) Discriminative joint probability maximum mean discrepancy (djp-mmd) for domain adaptation. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1–8

  61. Zhang Y, Zhang J, Guo X (2019) Kindling the darkness: a practical low-light image enhancer. In: Proceedings of the 27th ACM international conference on multimedia, pp 1632–1640

  62. Zhao D, Chen Y, Lv L (2017) Deep reinforcement learning with visual attention for vehicle classification. IEEE Trans Cognit Develop Syst 9(4):356–367

    Article  Google Scholar 

  63. Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision, pp 2223–2232

  64. Zhu Y, Zhuang F, Wang J, Chen J, Shi Z, Wu W, He Q (2019) Multi-representation adaptation network for cross-domain image classification. Neural Netw 119:214–221

    Article  Google Scholar 

  65. Zhuang P, Ding X (2020) Underwater image enhancement using an edge-preserving filtering retinex algorithm. Multimed Tools Appl 79(25):17257–17277

    Article  Google Scholar 

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Acknowledgements

This study was partly supported by grants from the National Natural Science Foundation of China (No.62076086 and No.61906061), Key Research and Development Program in Anhui Province (No.202004d07020008), and Scientific and Technological Achievements Cultivation Project of Intelligent Manufacturing Institute of HFUT (No. IMIPY2021022).

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Authors and Affiliations

  1. Key Laboratory of Knowledge Engineering With Big Data, Ministry of Education, Hefei University of Technology, Hefei, 230009, China

    Ye Yu

  2. School of Computer Science and Information, Hefei University of Technology, Hefei, 230009, China

    Ye Yu, Weixiao Chen, Fengxin Chen, Wei Jia & Qiang Lu

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  1. Ye Yu
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Correspondence to Wei Jia.

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Yu, Y., Chen, W., Chen, F. et al. Night-time vehicle model recognition based on domain adaptation. Multimed Tools Appl 83, 9577–9596 (2024). https://doi.org/10.1007/s11042-023-15447-1

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