Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 14 Sayı: 1

Taxonomy of sensor fusion techniques for various application areas: A review

Çeşitli uygulama alanları için sensör füzyon tekniklerinin taksonomisi: Bir inceleme

Hande Çavşi Zaim(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0002-9032-5145
Esra N. Yolaçan(ESKİŞEHİR OSMANGAZİ ÜNİVERSİTESİ)0000-0002-0008-1037
Pages
392–411
Publication Date
2025-01-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Thus, it contributes to advancing research in sensor fusion and developing more effective systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Sensor fusion techniques play critical roles in various industries such as defense, automotive, military, and healthcare.
📊
Key Findings & Data:These techniques allow systems to operate more efficiently.
💡
Conclusion & Impact:Thus, it contributes to advancing research in sensor fusion and developing more effective systems.
🔬Design:Derleme
📍Scope:Comprehensive Sample
⚙️Variables:Sensör füzyonu · Veri füzyonu · Bilgi füzyonu

Abstract

Sensor fusion techniques play critical roles in various industries such as defense, automotive, military, and healthcare. These techniques combine data from multiple sources, resulting in more detailed and reliable results. Sensor fusion techniques are indispensable for effective decision-making processes, especially in complex environments and variable conditions. These techniques allow systems to operate more efficiently. This study examines the advantages, challenges, and different algorithms used in various sensor fusion techniques and provides a comprehensive classification. This classification makes it possible to evaluate sensor fusion techniques and categorize them to appeal to broader applications. The study aims to help researchers understand sensor fusion techniques and guide them in making choices that suit their needs. Additionally, when evaluating the future potential of sensor fusion, the focus is on how fusion techniques may evolve, particularly with increasing complexity and diversity. Thus, it contributes to advancing research in sensor fusion and developing more effective systems.

Özet

Sensör füzyon teknikleri savunma, otomotiv, askeri ve sağlık gibi çeşitli endüstrilerde kritik rol oynamaktadır. Bu teknikler, birden fazla kaynaktan gelen verileri birleştirerek daha ayrıntılı ve güvenilir sonuçların elde edilmesini sağlar. Özellikle karmaşık ortamlarda ve değişken koşullarda etkili karar verme süreçleri için vazgeçilmez olan sensör füzyon teknikleri sistemlerin daha verimli çalışmasına olanak tanır. Bu çalışma, çeşitli sensör füzyon tekniklerinin avantajlarını, zorluklarını ve kullanılan farklı algoritmaları detaylı bir şekilde incelemekte ve kapsamlı bir sınıflandırma sunmaktadır. Bu sınıflandırma, çeşitli sensör füzyon tekniklerini değerlendirmeyi ve bunları daha geniş bir uygulama alanına hitap edecek şekilde kategorilere ayırmayı mümkün kılar. Çalışmanın amacı araştırmacılara sensör füzyon tekniklerini daha iyi anlamalarını sağlamak ve ihtiyaçlarına uygun seçimler yapmaları için rehberlik etmektir. Ayrıca, sensör füzyonunun gelecekteki potansiyeli değerlendirilirken, özellikle artan karmaşıklık ve çeşitlilikle birlikte füzyon tekniklerinin nasıl gelişebileceğine değinilmiştir. Böylece sensör füzyonunda araştırmaların ilerlemesine ve daha etkili sistemlerin geliştirilmesine katkı sağlanır.

🔖

Cite This Article

Zaim, H. Ç., Yolaçan, E. N. (2025). Taxonomy of sensor fusion techniques for various application areas: A review. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 14 Sayı: 1, 392–411. https://doi.org/10.28948/ngumuh.1455086

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References (98)

  1. F. E. White, JDL, Data Fusion Lexicon, Technical Panel for C3 15, 1991.
  2. Hall, L. David, J. Llinas. An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), pp. 6-23, 1997. https://doi.org/10.1109/5.554205
  3. F. Castanedo, A review of data fusion techniques. The Scientific World Journal, 2013(1), 704504, 2013. https://doi.org/10.1155/2013/704504
  4. K. S. Nagla, M. Uddin, D. Singh. Multisensor data fusion and integration for mobile robots: a review. IAES International Journal of Robotics and Automation, 3(2), pp. 131, 2014.
  5. H.F. Durrant-Whyte. Sensor models and multisensor integration. The İnternational Journal of Robotics Research, 7(6), pp. 97-113, 1988. https://doi.org/10.1177/027836498800700608
  6. R.C. Luo, M. G. Kay. A tutorial on multisensor integration and fusion. In Proceedings IECON'90: 16th Annual Conference of IEEE Industrial Electronics Society, pp. 707-722, 1990. https://doi.org/10.1109/IECON.1990.149228
  7. J. Llinas, D. L. Hall, An introduction to multi-sensor data fusion. In: ISCAS'98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems, 6, pp. 537-540, May 1998. https://doi.org/10.1109/ISCAS.1998.705329
  8. M. B. Alatise, G. P. Hancke, A review on challenges of autonomous mobile robot and sensor fusion methods. IEEE Access, 8, 39830-39846, 2020. https://doi.org/10.1109/ACCESS.2020.2975643
  9. M. L. Fung, M. Z. Chen, Y. H. Chen, Sensor fusion: A review of methods and applications. In 2017 29th Chinese Control and Decision Conference (CCDC), pp. 3853-3860, IEEE, May 2017. https://doi.org/10.1109/CCDC.2017.7979175
  10. K. Akkaya, I. Guvenc, R. Aygun, N. Pala, A. Kadri, IoT-based occupancy monitoring techniques for energy-efficient smart buildings. In 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 58-63, IEEE, March 2015. https://doi.org/10.1109/WCNCW.2015.7122529
  11. N. Ouerhani, N. Pazos, M. Aeberli, M. Muller, IoT-based dynamic street light control for smart cities use cases. In 2016 International Symposium on Networks, Computers and Communications (ISNCC), pp. 1-5, IEEE, May 2016. https://doi.org/10.1109/ISNCC.2016.7746112
  12. F. Kirsch, R. Miesen, M. Vossiek, Precise local-positioning for autonomous situation awareness in the Internet of Things. In 2014 IEEE MTT-S International Microwave Symposium (IMS2014), pp. 1-4, June 2014. https://doi.org/10.1109/MWSYM.2014.6848674
  13. C. L. Wu, Y. Xie, S. K. Pradhan, L. C. Fu, Y. C. Zeng, Unsupervised context discovery based on hierarchical fusion of heterogeneous features in real smart living environments. In 2016 IEEE International Conference on Automation Science and Engineering (CASE), pp. 1106-1111, IEEE, August 2016. https://doi.org/10.1109/COASE.2016.7743528
  14. S. K. Datta, R. P. F. Da Costa, J. Härri, C. Bonnet, Integrating connected vehicles in Internet of Things ecosystems: Challenges and solutions. In 2016 IEEE 17th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1-6, IEEE, June 2016. https://doi.org/10.1109/WoWMoM.2016.7523574
  15. S. Wildstrom, Better living through big data. Cisco Newsroom. Available: http://newsroom.cisco.com/feature/778800/Better-Living-Through-Big-Data, Accessed 11 March 2024.
  16. W. Ding, X. Jing, Z. Yan, and L. T. Yang, A survey on data fusion in internet of things: Towards secure and privacy-preserving fusion, Information Fusion, vol. 51, pp. 129-144, 2019. https://doi.org/10.1016/j.inffus.2018.12.001
  17. A. Westenberger, M. Muntzinger, M. Gabb, M. Fritzsche, and K. Dietmayer, Time-to-collision estimation in automotive multi-sensor fusion with delayed measurements, in Advanced Microsystems for Automotive Applications 2013: Smart Systems for Safe and Green Vehicles, Springer International Publishing, pp. 13-20, 2013. https://doi.org/10.1007/978-3-319-00476-1_2
  18. X. Wei, Autonomous control system for the quadrotor unmanned aerial vehicle, in 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), IEEE, pp. 796-799, August 2016. https://doi.org/10.1109/URAI.2016.7733984
  19. H. C. Longuet-Higgins, A computer algorithm for reconstructing a scene from two projections, Nature, 293(5828), pp. 133-135, 1981. https://doi.org/10.1038/293133a0
  20. E. M. Upadhyay and N. K. Rana, "Exposure fusion for concealed weapon detection," in 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), pp. 1-6, IEEE, March 2014. https://doi.org/10.1109/ICDCSyst.2014.6926141
  21. A. M. Sagi-Dolev, Multi-threat detection system, U.S. Patent 8171810, 2012.
  22. J. Favre, B. M. Jolles, O. Siegrist, and K. Aminian, Quaternion-based fusion of gyroscopes and accelerometers to improve 3D angle measurement, Electronics Letters, 42(11), p. 1, 2006. https://doi.org/10.1049/el:20060124
  23. G. Koshmak, A. Loutfi, M. Linden, Challenges and issues in multisensor fusion approach for fall detection. Journal of Sensors, 2016(1), 6931789, 2016. https://doi.org/10.1155/2016/6931789
  24. A. Rihar, M. Mihelj, J. Pašič, J. Kolar, M. Munih, Using sensory data fusion methods for infant body posture assessment. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 292-297, September 2015. https://doi.org/10.1109/IROS.2015.7353388
  25. S. Knoop, S. Vacek, R. Dillmann, Sensor fusion for 3D human body tracking with an articulated 3D body model. In Proceedings 2006 IEEE International Conference on Robotics and Automation, ICRA, pp. 1686-1691, May 2006. https://doi.org/10.1109/ROBOT.2006.1641949
  26. M. T. Yang, S. Y. Huang, Appearance-based multimodal human tracking and identification for healthcare in the digital home. Sensors, 14(8), 14253-14277, 2014. https://doi.org/10.3390/s140814253
  27. S. Begum, S. Barua, M. U. Ahmed, Physiological sensor signals classification for healthcare using sensor data fusion and case-based reasoning. Sensors, 14(7), 11770-11785, 2014. https://doi.org/10.3390/s140711770
  28. D. J. Yeong, G. Velasco-Hernandez, J. Barry, J. Walsh, Sensor and sensor fusion technology in autonomous vehicles: a review. Sensors, 21(6), 2140, 2021. https://doi.org/10.3390/s21062140
  29. W. Elmenreich, An introduction to sensor fusion. Vienna University of Technology, Austria, 502, pp. 1-28, 2002.
  30. A. Deo, V. Palade, M. N. Huda, Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist. Sensors, 21(16), 5422, 2021. https://doi.org/10.3390/s21165422
  31. J. Fayyad, M. A. Jaradat, D. Gruyer, H. Najjaran, Deep learning sensor fusion for autonomous vehicle perception and localization: a review. Sensors, 20(15), 4220, 2020. https://doi.org/10.3390/s20154220
  32. M. Almasri, K. Elleithy, Data fusion in WSNs: architecture, taxonomy, evaluation of techniques, and challenges. Int J Sci Eng Res, 6(4), 1620-36, 2015.
  33. K. Huang, B. Shi, X. Li, S. Huang, Y. Li, Multi-modal sensor fusion for auto driving perception: a survey. arXiv preprint arXiv:2202.02703, 2022. https://doi.org/10.48550/arXiv.2202.02703
  34. Y. Cui, R. Chen, W. Chu, L. Chen, D. Tian, Y. Li, D. Cao, Deep learning for image and point cloud fusion in autonomous driving: a review. IEEE Transactions on Intelligent Transportation Systems, 23(2), 722-739, 2021. https://doi.org/10.1109/TITS.2020.3023541
  35. Y. Wang, Q. Mao, H. Zhu, J. Deng, Y. Zhang, J. Ji, Y. Zhang, Multi-modal 3d object detection in autonomous driving: a survey. International Journal of Computer Vision, 131(8), 2122-2152, 2023. https://doi.org/10.1007/s11263-023-01784-z
  36. H. F. Durrant-Whyte, Sensor models and multisensor integration. The İnternational Journal Of Robotics Research, 7(6), 97-113, 1988. https://doi.org/10.1177/027836498800700608
  37. R. Willett, A. Martin, R. Nowak, Backcasting: adaptive sampling for sensor networks. In Proceedings of the 3rd İnternational Symposium On Information Processing İn Sensor Networks, pp. 124-133, April 2004. https://doi.org/10.1145/984622.984641
  38. V. Gupta, R. Pandey, Data fusion and topology control in wireless sensor networks. WSEAS Transactions on Signal Processing, 4(4), 150-172, 2008.
  39. K. Qian, S. Zhu, X. Zhang, L. E. Li, Robust multimodal vehicle detection in foggy weather using complementary lidar and radar signals. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 444-453, 2021.
  40. X. Xu, L. Zhang, J. Yang, C. Cao, W. Wang, Y. Ran, M. Luo, A review of multi-sensor fusion slam systems based on 3D LIDAR. Remote Sensing, 14(12), 2835, 2022. https://doi.org/10.3390/rs14122835
  41. J. Alfred Daniel, C. Chandru Vignesh, B. A. Muthu, R. Senthil Kumar, C. B. Sivaparthipan, C. E. M. Marin, Fully convolutional neural networks for LIDAR–camera fusion for pedestrian detection in autonomous vehicle. Multimedia Tools and Applications, 82(16), 25107-25130, 2023. https://doi.org/10.1007/s11042-023-14417-x
  42. E. F. Nakamura, A. A. Loureiro, A. C. Frery, Information fusion for wireless sensor networks: methods, models, and classifications. ACM Computing Surveys (CSUR), 39(3), 9-es, 2007. https://doi.org/10.1145/1267070.1267073
  43. J. Samatha, G. Madhavi, Securesense: enhancing person verification through multimodal biometrics for robust authentication. Scalable Computing: Practice and Experience, 25(2), 1040-1054, 2024. https://doi.org/10.12694/scpe.v25i2.2524
  44. A. Rehman, T. Saba, M. Kashif, S. M. Fati, S. A. Bahaj, H. Chaudhry, A revisit of internet of things technologies for monitoring and control strategies in smart agriculture. Agronomy, 12(1), 127, 2022. https://doi.org/10.3390/agronomy12010127
  45. B. V. Dasarathy, Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proceedings of the IEEE, 85(1), 24-38, 1997. https://doi.org/10.1109/5.554206
  46. E. Alpaydin, Classifying multimodal data. In The Handbook of Multimodal-Multisensor Interfaces: Signal Processing, Architectures, and Detection of Emotion and Cognition, 2, pp. 49-69, 2018. https://doi.org/10.1145/3107990.3107994
  47. P. A. Neves, J. J. Rodrigues, K. Lin, Data fusion on wireless sensor and actuator networks powered by the ZenSens system. IET communications, 5(12), 1661-1668, 2011. https://doi.org/10.1049/iet-com.2010.0644
  48. C. Zhao, Y. Wang, A new classification method on information fusion of wireless sensor networks. In 2008 International Conference on Embedded Software and Systems Symposia, pp. 231-236, IEEE, July 2008. https://doi.org/10.1109/ICESS.Symposia.2008.73
  49. K. Maraiya, K. Kant, N. Gupta, Study of data fusion in wireless sensor network. In International Conference on Advanced Computing and Communication Technologies, pp. 535-539, 2011.
  50. M. E. Liggins, C. Y. Chong, I. Kadar, M. G. Alford, V. Vannicola, S. Thomopoulos, Distributed fusion architectures and algorithms for target tracking. Proceedings of the IEEE, 85(1), 95-107, 1997. https://doi.org/10.1109/JPROC.1997.554211
  51. E. Waltz, Multisensor Data Fusion, Boston: Artech House, 685, 1990.
  52. E. Azimirad, J. Haddadnia, The comprehensive review on JDL model in data fusion networks: techniques and methods. International Journal of Computer Science and Information Security, 13(1), 53, 2015.
  53. D. E. Moshou, X. E, Pantazi, Data fusion and its applications in agriculture. In Information and Communication Technologies for Agriculture—Theme II: Data, pp. 17-40, Springer International Publishing, 2002. https://doi.org/10.1007/978-3-030-84148-5_2
  54. I. Ermolov, A novel data fusion architecture for uncrewed vehicles. In 21TH International Symposium On Measurement And Control In Robotics ISMCR, pp. 26-28, 2018. https://doi.org/10.21014/acta_imeko.v8i4.684
  55. M. Markin, C. Harris, M. Bernhardt, J. Austin, M. Bedworth, P. Greenway, D. Lowe, Technology foresight on data fusion and data processing. Publication of The Royal Aeronautical Society, 1997.
  56. A. N. Afif, F. Noviyanto, S. Sunardi, S. A. Akbar, E. Aribowo, Integrated application for automatic schedule-based distribution and monitoring of irrigation by applying the waterfall model process. Bulletin of Electrical Engineering and Informatics, 9(1), 420-426, 2020. https://doi.org/10.11591/eei.v9i1.1368
  57. C. Wang, T. Ji, F. Mao, Z. Wang, Z. Li, Prognostics and health management system for electric vehicles with a hierarchy fusion framework: concepts, architectures, and methods. Advances in Civil Engineering, 2021(1), 6685900, 2021. https://doi.org/10.1155/2021/6685900
  58. M. Bevilacqua, A. Tsourdos, A. Starr, I. Durazo-Cardenas, Data fusion strategy for precise vehicle location for intelligent self-aware maintenance systems. In 2015 6th International Conference on Intelligent Systems, Modelling and Simulation, pp. 76-81, IEEE, 2015. https://doi.org/10.1109/ISMS.2015.37
  59. J. R. Boyd, A discourse on winning and losing. Maxwell Air Force Base, AL: Air University Press, 400, 2018.
  60. M. Bedworth, J. O'Brien, The Omnibus model: a new model of data fusion?. IEEE Aerospace and Electronic Systems Magazine, 15(4), 30-36, 2000. https://doi.org/10.1109/62.839632
  61. R. Alami, R. Chatila, S. Fleury, M. Ghallab, F. Ingrand, An architecture for autonomy. The International Journal of Robotics Research, 17(4), 315-337, 1998. https://doi.org/10.1177/027836499801700402
  62. Y. Tao, H. Hu, H. Zhou, Integration of vision and inertial sensors for 3D arm motion tracking in home-based rehabilitation. The International Journal of Robotics Research, 26(6), 607-624, 2007. https://doi.org/10.1177/0278364907079278
  63. M. A. Lee, B. Yi, R. Martín-Martín, S. Savarese, J. Bohg, Multimodal sensor fusion with differentiable filters. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 10444-10451, IEEE, October 2020. https://doi.org/10.1109/IROS45743.2020.9341579
  64. Z. Huang, C. Lv, Y. Xing, J. Wu, Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding. IEEE Sensors Journal, 21(10), 11781-11790, 2020. https://doi.org/10.1109/JSEN.2020.3003121
  65. K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, A.Geiger, Transfuser: Imitation with transformer-based sensor fusion for autonomous driving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. https://doi.org/10.1109/TPAMI.2022.3200245
  66. Y. Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, A. M. López, Multimodal end-to-end autonomous driving. IEEE Transactions on Intelligent Transportation Systems, 23(1), 537-547, 2020. https://doi.org/10.1109/TITS.2020.3013234
  67. Z. Liu, H. Tang, A. Amini, X. Yang, H. Mao, D. L. Rus, S. Han, Bevfusion: Multi-task multi-sensor fusion with unified bird's-eye view representation. In 2023 IEEE international conference on robotics and automation (ICRA), pp. 2774-2781, IEEE, 2023. https://doi.org/10.1109/ICRA48891.2023.10160968
  68. A. Singh, Transformer-based sensor fusion for autonomous driving: A survey. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3312-3317, 2023.
  69. K. Lin, Y. Li, J. Sun, D. Zhou, Q. Zhang, Multi-sensor fusion for body sensor network in medical human–robot interaction scenario. Information Fusion, 57, pp. 15-26, 2020. https://doi.org/10.1016/j.inffus.2019.11.001
  70. M. Z. Uddin, M. M. Hassan, A. Alsanad, C. Savaglio, A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Information Fusion, 55, 105-115, 2020. https://doi.org/10.1016/j.inffus.2019.08.004
  71. S. Chung, J. Lim, K. J. Noh, G. Kim, H. Jeong, Sensor data acquisition and multimodal sensor fusion for human activity recognition using deep learning. Sensors, 19(7), 1716, 2019. https://doi.org/10.3390/s19071716
  72. G. Shi, C. Geng, H. Liu, H. Su, Y. Jin, S. Sun, The human body characteristic parameters extraction and disease tendency prediction based on multi-sensing fusion algorithms. In 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 126-130, IEEE, June 2016. https://doi.org/10.1109/CYBER.2016.7574808
  73. A. Alofi, A. Alghamdi, R. Alahmadi, N. Aljuaid, M. Hemalatha, A review of data fusion techniques. International Journal of Computer Applications, 167(7), pp. 37-41, 2017. https://doi.org/10.5120/ijca2017914318
  74. Q. Li, C. Zhang, Q. Hu, H. Fu, P. Zhu, Confidence-aware fusion using dempster-shafer theory for multispectral pedestrian detection. IEEE Transactions on Multimedia, 2022. https://doi.org/10.1109/TMM.2022.3160589
  75. M. O. Oloyede, G. P. Hancke, Unimodal and multimodal biometric sensing systems: a review. IEEE Access, 4, 7532-7555, 2016. https://doi.org/10.1109/ACCESS.2016.2614720
  76. K. Khoshelham, S. Nedkov, C. Nardinocchi, A comparison of Bayesian and evidence-based fusion methods for automated building detection in aerial data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(PART B7), 1183-1188, 2008.
  77. M. Z. Uddin, M. M. Hassan, A. Alsanad, C. Savaglio, A body sensor data fusion and deep recurrent neural network-based behaviour recognition approach for robust healthcare. Information Fusion, 55, 105-115, 2020. https://doi.org/10.1016/j.inffus.2019.08.004
  78. S. Münzner, P. Schmidt, A. Reiss, M. Hanselmann, R. Stiefelhagen, R. Dürichen, CNN-based sensor fusion techniques for multimodal human activity recognition. In Proceedings of the 2017 ACM International Symposium On Wearable Computers, pp. 158-165, September 2017. https://doi.org/10.1145/3123021.3123046
  79. R. Girshick, J. Donahue, T. Darrell, J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference On Computer Vision And Pattern Recognition, pp. 580-587, 2014. https://doi.org/10.1109/CVPR.2014.81
  80. J. Wagner, V. Fischer, M. Herman, S. Behnke, Multispectral Pedestrian Detection using Deep Fusion Convolutional Neural Networks. In ESANN, 587, pp. 509-514, April 2016.
  81. K. He, X. Zhang, S. Ren, J. Sun, Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions On Pattern Analysis And Machine İntelligence, 37(9), 1904-1916, 2015. https://doi.org/10.1109/TPAMI.2015.2389824
  82. R. Girshick, Fast r-cnn. In Proceedings of the IEEE İnternational Conference On Computer Vision, pp. 1440-1448, 2015. https://doi.org/10.1109/ICCV.2015.169
  83. S. Ren, K. He, R. Girshick, J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions On Pattern Analysis And Machine İntelligence, 39(6), 1137-1149, 2016. https://doi.org/10.1109/TPAMI.2016.2577031
  84. J. Liu, S. Zhang, S. Wang, D. N. Metaxas, Multispectral deep neural networks for pedestrian detection. arXiv preprint arXiv:1611.02644, 2016. https://doi.org/10.48550/arXiv.1611.02644
  85. J., Redmon, S. Divvala, R. Girshick, A. Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition (pp. 779-788), 2016. https://doi.org/10.1109/CVPR.2016.91
  86. J. Redmon, A. Farhadi, YOLO9000: better, faster, stronger. In Proceedings of the IEEE Conference on Computer Vision And Pattern Recognition, pp. 7263-7271, 2017. https://doi.org/10.1109/CVPR.2017.690
  87. J. Redmon, A. Farhadi, Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. https://doi.org/10.48550/arXiv.1804.02767
  88. A. Asvadi, L. Garrote, C. Premebida, P. Peixoto, U. J. Nunes, Multimodal vehicle detection: fusing 3D-LIDAR and color camera data. Pattern Recognition Letters, 115, 20-29, 2018. https://doi.org/10.1016/j.patrec.2018.04.011
  89. H. Wang, X. Lou, Y. Cai, Y. Li, L. Chen, Real-time vehicle detection algorithm based on vision and lidar point cloud fusion. Journal of Sensors, 2019. https://doi.org/10.1155/2019/8473980
  90. J. Dou, J. Fang, T. Li, J. Xue, Boosting cnn-based pedestrian detection via 3d lidar fusion in autonomous driving. In Image and Graphics: 9th International Conference, ICIG 2017, Shanghai, China, September 13-15, 2017, Revised Selected Papers, Part II 9, pp. 3-13, Springer International Publishing, 2017. https://doi.org/10.1007/978-3-319-71589-6_1
  91. J. Han, Y. Liao, J. Zhang, S. Wang, S. Li, Target fusion detection of LiDAR and camera based on the improved YOLO algorithm. Mathematics, 6(10), 213, 2018. https://doi.org/10.3390/math6100213
  92. W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg, Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14, Proceedings, Part I 14, pp. 21-37, Springer International Publishing, October 2016. https://doi.org/10.1007/978-3-319-46448-0_2
  93. J. Kim, J. Choi, Y. Kim, J. Koh, C. C. Chung, J. W. Choi, Robust camera lidar sensor fusion via deep gated information fusion network. In 2018 IEEE Intelligent Vehicles Symposium (IV), pp. 1620-1625, IEEE, June 2018. https://doi.org/10.1109/IVS.2018.8500711
  94. Y. L. Hou, Y. Song, X. Hao, Y. Shen, M. Qian, H. Chen, Multispectral pedestrian detection based on deep convolutional neural networks. Infrared Physics & Technology, 94, 69-77, 2018. https://doi.org/10.1016/j.infrared.2018.08.029
  95. Z. Li, L. Yang, F. Zhou, FSSD: feature fusion single shot multibox detector. arXiv preprint arXiv:1712.00960, 2017. https://doi.org/10.48550/arXiv.1712.00960
  96. C. Y. Fu, W. Liu, A. Ranga, A. Tyagi, A. C. Berg, Dssd: Deconvolutional single shot detector. arXiv preprint arXiv:1701.06659, 2017. https://doi.org/10.48550/arXiv.1701.06659
  97. Y. Lee, T. D. Bui, J. Shin, Pedestrian detection based on deep fusion network using feature correlation. In 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), pp. 694-699, IEEE, November 2018. https://doi.org/10.23919/APSIPA.2018.8659688
  98. Y. Zhao, R. Govindan, D. Estrin, Residual energy scans for monitoring wireless sensor networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC'02), IEEE, Orlando, pp. 356 362, 2002. https://doi.org/10.1109/WCNC.2002.993521