Advance generalization technique through 3D CNN to overcome the false positives pedestrian in autonomous vehicles

Iftikhar, Sundas; Asim, Muhammad; Zhang, Zuping; El‐Latif, Ahmed A. Abd

doi:10.1007/s11235-022-00930-1

Cited by 14 publications

(7 citation statements)

References 28 publications

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“…The cutting-edge of contemporary automotive technology is represented by automated driving systems (ADS) and autonomous vehicles (AVs). AVs, especially self-driving cars, have generated a great deal of interest in the fields of intelligent transportation, computer vision, and artificial intelligence [2]. No other invention since the creation of vehicles has had such a dramatic impact on the automotive industry as self-driving vehicles.…”

Section: Introductionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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Enhancing the environmental perception of autonomous vehicles (AVs) in intelligent transportation systems requires computer vision technology to be effective in detecting objects and obstacles, particularly in adverse weather conditions. Adverse weather circumstances present serious difficulties for object-detecting systems, which are essential to contemporary safety procedures, infrastructure for monitoring, and intelligent transportation. AVs primarily depend on image processing algorithms that utilize a wide range of onboard visual sensors for guidance and decisionmaking. Ensuring the consistent identification of critical elements such as vehicles, pedestrians, and road lanes, even in adverse weather, is a paramount objective. This paper not only provides a comprehensive review of the literature on object detection (OD) under adverse weather conditions but also delves into the ever-evolving realm of the architecture of AVs, challenges for automated vehicles in adverse weather, the basic structure of OD, and explores the landscape of traditional and deep learning (DL) approaches for OD within the realm of AVs. These approaches are essential for advancing the capabilities of AVs in recognizing and responding to objects in their surroundings. This paper further investigates previous research that has employed both traditional and DL methodologies for the detection of vehicles, pedestrians, and road lanes, effectively linking these approaches with the evolving field of AVs. Moreover, this paper offers an in-depth analysis of the datasets commonly employed in AV research, with a specific focus on the detection of key elements in various environmental conditions, and then summarizes the evaluation matrix. We expect that this review paper will help scholars to gain a better understanding of this area of research.

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Section: Introductionmentioning

confidence: 99%

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Tahir,

Zhang,

Asim

et al. 2024

Algorithms

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“…Furthermore, their deployment can address one of the most pressing issues of road safety, namely, accidents caused by human error (Ahmed et al, 2022). As per the World Health Organization (WHO), a staggering 1.35 million lives are lost annually worldwide due to road accidents and bad climate (Iftikhar et al, 2022;Rehan et al, 2023a;Rehan et al, 2023b). The advent of autonomous vehicles promises to curb this distressing statistic (Iftikhar et al, 2022) significantly.…”

Section: Introductionmentioning

confidence: 99%

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Khan,

Raza,

Abbas

et al. 2024

Front. Built Environ.

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Autonomous vehicles can transform the transportation sector by offering a safer and more effective means of travel. However, the success of self-driving cars depends on their ability to navigate complex road conditions, including the detection of potholes. Potholes pose a substantial risk to vehicles and passengers, leading to potential damage and safety hazards, making their detection a critical task for autonomous driving. In this work, we propose a robust and efficient solution for pothole detection using the “you look only once (YOLO) algorithm of version 8, the newest deep learning object detection algorithm.” Our proposed system employs a deep learning methodology to identify real-time potholes, enabling autonomous vehicles to avoid potential hazards and minimise accident risk. We assess the effectiveness of our system using publicly available datasets and show that it outperforms existing state-of-the-art approaches in terms of accuracy and efficiency. Additionally, we investigate different data augmentation methods to enhance the detection capabilities of our proposed system. Our results demonstrate that YOLO V8-based pothole detection is a promising solution for autonomous driving and can significantly improve the safety and reliability of self-driving vehicles on the road. The results of our study are also compared with the results of YOLO V5.

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“…Target detection is utilized to find out where targets are located in the area and which group they are associated with such as pedestrians [1], vehicles [2], etc. Many researchers tried to handle the target detection problem in various fields such as solving the target detection in the form of pedestrians in autonomous vehicles under different sub-issues such as occlusion, large and small-size objects, and complex backgrounds with the use of various strategies [3,4]. However, in this review paper, we study target detection through UAVs.…”

Section: Introductionmentioning

confidence: 99%

Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis

et al. 2023

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In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people’s lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends.

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Advance generalization technique through 3D CNN to overcome the false positives pedestrian in autonomous vehicles

Cited by 14 publications

References 28 publications

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Object Detection in Autonomous Vehicles under Adverse Weather: A Review of Traditional and Deep Learning Approaches

Pothole detection for autonomous vehicles using deep learning: a robust and efficient solution

Target Detection and Recognition for Traffic Congestion in Smart Cities Using Deep Learning-Enabled UAVs: A Review and Analysis

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