Download pdf
Research on target detection and recognition system of autonomous driving based on deep learning
- 1 Beijing-Dublin International college, University of Technology, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
Autonomous driving technology relies heavily on advanced target detection and recognition systems to ensure safety and efficiency. This essay explores how deep learning has revolutionized these systems in four key areas: lane detection, detection of obscured vehicle parts, blind spot detection, and multi-target detection. Traditional methods often struggle under challenging conditions, but deep learning approaches, including Convolutional Neural Networks (CNNs) and Fully Convolutional Networks (FCNs), offer superior performance by analyzing complex features from raw data. Techniques like the Detection of Incomplete Vehicles using Deep Learning and Image Inpainting (DIDA) enhance safety by reconstructing obscured vehicle parts. In blind spot detection, models such as Sep-Res-SE blocks provide an effective, cost-efficient alternative to radar systems. The Adaptive Perceive SSD (AP-SSD) framework improves multi-target detection accuracy and real-time tracking by incorporating advanced feature extraction and temporal analysis. The essay concludes with future research directions aimed at refining real-time capabilities, expanding datasets, and exploring collaborative learning to further enhance autonomous driving technology.
Keywords
Autonomous driving, target detection, target recognition, deep learning.
[1]. Kang K, Park N, Park J and Abdel-Aty M 2024 Deep Q-network learning-based active speed management under autonomous driving environments Comput.-Aided Civ. Infrastruct. Eng. 23
[2]. Singal G, Singhal H, Kushwaha R, Veeramsetty V, Badal T and Lamba S 2023 RoadWay: lane detection for autonomous driving vehicles via deep learning Multimedia Tools Appl. 82 4965-4978
[3]. Wang L, Wu Y, Zhang S, Huang Y and Zhang X 2023 Research on Combat Effectiveness Based on Internet of Things IEEE 167
[4]. Laurell C and Sandstrom C 2022 Social Media Analytics as an Enabler for External Search and Open Foresight-The Case of Tesla's Autopilot and Regulatory Scrutiny of Autonomous Driving IEEE Trans. Eng. Manage. 69 564-571
[5]. Lee S D, Tzeng C Y, Kehr Y Z, Huang C C and Kang C K 2010 Autopilot System Based on Color Recognition Algorithm and Internal Model Control Scheme for Controlling Approaching Maneuvers of a Small Boat IEEE J. Ocean. Eng. 35 376-387
[6]. Tseng Y W, Hung T W, Pan C L and Wu R C 2019 Motion control system of unmanned railcars based on image recognition Appl. Syst. Innov. 2 1-16
[7]. Hoque S, Xu S, Maiti A, Wei Y and Arafat M Y 2023 Deep learning for 6D pose estimation of objects — A case study for autonomous driving Expert Syst. Appl. 223 119838
[8]. The Tesla Team A Tragic Loss Available online: https://www.tesla.com/blog/tragic-loss
[9]. Xu Y, Wang H, Liu X, He H R, Gu Q and Sun W 2019 Learning to see the hidden part of the vehicle in the autopilot scene Electronics (Basel) 8 331
[10]. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A and Chen X 2016 Improved Techniques for Training GANs In: Advances in Neural Information Processing Systems 2234-2242
[11]. Smirnov E A, Timoshenko D M and Andrianov S N 2014 Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks AASRI Procedia 6 89-94
[12]. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A 2015 Going deeper with convolutions IEEE 1
[13]. Simonyan K and Zisserman A 2015 Very Deep Convolutional Networks for Large-Scale Image Recognition Cornell University Library arXiv.org Ithaca
[14]. He K, Zhang X, Ren S and Sun J 2016 Deep Residual Learning for Image Recognition IEEE 770
[15]. Lin T, Goyal P, Girshick R, He K and Dollar P 2017 Focal Loss for Dense Object Detection IEEE 2999
[16]. Redmon J and Farhadi A 2017 YOLO9000: Better, Faster, Stronger IEEE 6517
[17]. Redmon J, Divvala S, Girshick R and Farhadi A 2016 You Only Look Once: Unified, Real-Time Object Detection IEEE 779
[18]. Ramteke A Y, Ramteke P, Dhongade A, Modak U and Thakre L P 2024 Blind Spot Detection for Autonomous Driving Using RADAR Technique J. Phys. Conf. Ser. 2763 12015
[19]. Jichkar R, Paraskar S, Parteki R, Ghosh M, Deotale T, Pathan A S, Bawankar S and Thakare L P 2023 5g: An Emerging Technology And Its Advancement IEEE 1
[20]. Zhao Y, Bai L, Lyu Y and Huang X 2019 Camera-based blind spot detection with a general purpose lightweight neural network Electronics (Basel) 8 233
[21]. Wang X, Hua X, Xiao F, Li Y, Hu X and Sun P 2018 Multi-object detection in traffic scenes based on improved SSD Electronics (Basel) 7 302
[22]. Chang C, Liou Y, Huang Y, Shen S, Yu P, Chuang T and Chiou S 2022 YOLO based deep learning on needle-type dashboard recognition for autopilot maneuvering system Meas. Control (Lond.) 55 567-582
[23]. Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C 2016 SSD: Single shot multibox detector 21
Cite this article
Ye,H. (2024). Research on target detection and recognition system of autonomous driving based on deep learning. Applied and Computational Engineering,101,138-146.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).