A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems
This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, an...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10818658/ |
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| author | Majdi Sukkar Rajendrasinh Jadeja Madhu Shukla Rajesh Mahadeva |
| author_facet | Majdi Sukkar Rajendrasinh Jadeja Madhu Shukla Rajesh Mahadeva |
| author_sort | Majdi Sukkar |
| collection | DOAJ |
| description | This paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems. |
| format | Article |
| id | doaj-art-04d5e2c5cc564ed4979ed6c05d09bc6b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-04d5e2c5cc564ed4979ed6c05d09bc6b2025-01-10T00:01:10ZengIEEEIEEE Access2169-35362025-01-01133994400710.1109/ACCESS.2024.352450110818658A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous SystemsMajdi Sukkar0https://orcid.org/0000-0002-4830-9096Rajendrasinh Jadeja1https://orcid.org/0000-0003-3466-203XMadhu Shukla2https://orcid.org/0000-0002-8023-7854Rajesh Mahadeva3https://orcid.org/0000-0001-8952-7172Department of Artificial Intelligence, Machine Learning and Data Science, Marwadi University, Rajkot, Gujarat, IndiaElectrical Engineering Department, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Artificial Intelligence, Machine Learning and Data Science, Marwadi University, Rajkot, Gujarat, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaThis paper surveys real-time object detection literature critically and analytically, focusing particularly on pedestrian detection for safe autonomous vehicles. It addresses the challenges in the domain, some of the sources of which are variations in age, gender, clothing, lighting, backgrounds, and occlusion. The paper reviews object detection algorithms after providing an overview of deep learning basics and main architectures of neural networks, followed by discussion on existing algorithms along with their strengths, weaknesses, and future research directions. There is a need for pedestrian detection datasets with further complex annotations and multi-source integration, which captures interactions between pedestrians and their surroundings. Incorporating advanced sensors, including LiDAR, infrared, and depth sensors, as the foremost means to enhance the detection capabilities in more adverse conditions, such as low-light situations and occlusion. However, architectures such as YOLO, SSD, and Faster R-CNN, which have led to current improvements in performance, still allow room for improving pedestrian detection accuracy. By filling in these insights and proposed solutions, the paper focus on the development of pedestrian detection technology, how it can be brought into a safer, reliable, real-world applicability towards the system of autonomous driving. All of these results point to continued innovation towards deep learning, multi-sensor integration, and developing datasets to achieve optimal performance levels in real world conditions for autonomous driving systems.https://ieeexplore.ieee.org/document/10818658/Artificial intelligenceautonomous vehiclecomputer visiondeep learningpedestrian detection |
| spellingShingle | Majdi Sukkar Rajendrasinh Jadeja Madhu Shukla Rajesh Mahadeva A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems IEEE Access Artificial intelligence autonomous vehicle computer vision deep learning pedestrian detection |
| title | A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems |
| title_full | A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems |
| title_fullStr | A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems |
| title_full_unstemmed | A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems |
| title_short | A Survey of Deep Learning Approaches for Pedestrian Detection in Autonomous Systems |
| title_sort | survey of deep learning approaches for pedestrian detection in autonomous systems |
| topic | Artificial intelligence autonomous vehicle computer vision deep learning pedestrian detection |
| url | https://ieeexplore.ieee.org/document/10818658/ |
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