Pedestrian Perception Tracking in Complex Environment of Unmanned Vehicles Based on Deep Neural Networks
DOI:
https://doi.org/10.4108/ew.5793Keywords:
YOLOv4, Driverless Vehicles, Complex Scene PerceptionAbstract
INTRODUCTION: In recent years, machine learning and deep learning have emerged as pivotal technologies with transformative potential across various industries. Among these, the automobile industry stands out as a significant arena for the application of these technologies, particularly in the development of smart cars with unmanned driving systems. This article delves into the extensive research conducted on the detection technology employed by autonomous vehicles to navigate road conditions, a critical aspect of driverless car technology.
OBJECTIVES: The primary aim of this research is to explore and highlight the intricacies of road condition detection for autonomous vehicles. Emphasizing the importance of this key component in the development of driverless cars, we aim to provide insights into cutting-edge algorithms that enhance the capabilities of these vehicles, ultimately contributing to their widespread adoption.
METHODS: In addressing the challenge of road condition detection, we introduce the TidyYOLOv4 algorithm. This algorithm, deemed more advantageous than YOLOv4, particularly excels in pedestrian recognition within urban traffic environments. Its real-time capabilities make it a suitable choice for detecting pedestrians on the road under dynamic conditions.
RESULTS: The application of the TidyYOLOv4 algorithm in autonomous vehicles has yielded promising results, especially in enhancing pedestrian recognition in urban traffic settings. The algorithm's real-time functionality proves crucial in ensuring the timely detection of pedestrians on the road, thereby improving the overall safety and efficiency of autonomous vehicles.
CONCLUSION: In conclusion, the detection of road conditions is a critical aspect of autonomous vehicle technology, with implications for safety and efficiency. The TidyYOLOv4 algorithm emerges as a noteworthy advancement, outperforming its predecessor YOLOv4 in pedestrian recognition within urban traffic environments. As companies continue to invest in driverless technology, leveraging such advanced algorithms becomes imperative for the successful deployment of autonomous vehicles in real-world scenarios.
Downloads
References
Jin Q, Cui H, Sun C, et al.Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images[J].Expert Systems with Applications,2021, 176. DOI: https://doi.org/10.1016/j.eswa.2021.114848
Li W, Raj A N J, Tjahjadi T, et al.Digital hair removal by deep learning for skin lesion segmentation[J].Pattern Recognition,2021, 117. DOI: https://doi.org/10.1016/j.patcog.2021.107994
Niehues S M, Adams L C, Gaudin R A, et al.Deep-Learning-Based Diagnosis of Bedside Chest X-ray in Intensive Care and Emergency Medicine[J].Investigative radiology,2021, 56 (8): 525-534. DOI: https://doi.org/10.1097/RLI.0000000000000771
Owais M, Yoon H S, Mahmood T, et al.Light-weighted ensemble network with multilevel activation visualization for robust diagnosis of COVID19 pneumonia from large-scale chest radiographic database[J].Applied Soft Computing,2021, 108. DOI: https://doi.org/10.1016/j.asoc.2021.107490
Onan A, Tocoglu M a L P.A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification[J].Ieee Access,2021, 9: 7701-7722. DOI: https://doi.org/10.1109/ACCESS.2021.3049734
Roh Y, Heo G, Whang S E.A Survey on Data Collection for Machine Learning: A Big Data-AI Integration Perspective[J].Ieee Transactions on Knowledge and Data Engineering,2021, 33 (4): 1328-1347. DOI: https://doi.org/10.1109/TKDE.2019.2946162
Wen S, Wei H, Yang Y, et al.Memristive LSTM Network for Sentiment Analysis[J].Ieee Transactions on Systems Man Cybernetics-Systems,2021, 51 (3): 1794-1804.
Yang Z-L, Zhang S-Y, Hu Y-T, et al.VAE-Stega: Linguistic Steganography Based on Variational Auto-Encoder[J].Ieee Transactions on Information Forensics and Security,2021, 16: 880-895. DOI: https://doi.org/10.1109/TIFS.2020.3023279
Burnett K, Qian J, Du X, et al.Zeus: A system description of the two-time winner of the collegiate SAE autodrive competition[J].Journal of Field Robotics,2021, 38 (1): 139-166. DOI: https://doi.org/10.1002/rob.21958
Burnett K, Samavi S, Waslander S L, et al.aUToTrack: a lightweight object detection and tracking system for the SAE autodrive challenge arXiv[J].arXiv,2019: 8 pp.-8 pp. DOI: https://doi.org/10.1109/CRV.2019.00036
Samak T V, Samak C V, Ming X.AutoDRIVE Simulator: A Simulator for Scaled Autonomous Vehicle Research and Education arXiv[J].arXiv,2021: 8 pp.-8 pp. DOI: https://doi.org/10.1145/3483845.3483846
Wen J, Chen B, Tang W, et al.Harsh-Environmental-Resistant Triboelectric Nanogenerator and Its Applications in Autodrive Safety Warning[J].Advanced Energy Materials,2018, 8 (29). DOI: https://doi.org/10.1002/aenm.201801898
WangNa. Research on pedestrian detection algorithm and its security in unmanned driving [D],NanJing University.2020.
Dai J, Li Y, He K, et al. R-FCN: Object Detection via Region-based Fully Convolutional Networks,2016: arXiv:1605.06409.
Girshick R, Donahue J, Darrell T, et al.: Rich feature hierarchies for accurate object detection and semantic segmentation,2014 Ieee Conference on Computer Vision and Pattern Recognition, New York: Ieee,2014: 580-587. DOI: https://doi.org/10.1109/CVPR.2014.81
Girshick R J a E-P. Fast R-CNN,2015: arXiv:1504.08083. DOI: https://doi.org/10.1109/ICCV.2015.169
He K, Zhang X, Ren S, et al. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition,2014: arXiv:1406.4729. DOI: https://doi.org/10.1007/978-3-319-10578-9_23
Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-Time Object Detection,2015: arXiv:1506.02640. DOI: https://doi.org/10.1109/CVPR.2016.91
Redmon J, Farhadi A J a E-P. YOLO9000: Better, Faster, Stronger,2016: arXiv:1612.08242. DOI: https://doi.org/10.1109/CVPR.2017.690
Redmon J, Farhadi A J a E-P. YOLOv3: An Incremental Improvement,2018: arXiv:1804.02767.
Bochkovskiy A, Wang C-Y, Liao H-Y M J a E-P. YOLOv4: Optimal Speed and Accuracy of Object Detection,2020: arXiv:2004.10934.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 EAI Endorsed Transactions on Energy Web
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
