Deep Learning Approaches for Vehicle and Pedestrian Detection in Adverse Weather

Zaman, Mostafa; Saha, Sujay; Zohrabi, Nasibeh; Abdelwahed, Sherif

doi:10.1109/itec55900.2023.10187020

Cited by 3 publications

(2 citation statements)

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“…The method's performance on open databases, with high mAP scores and IoU of 0.5 across various datasets, is encouraging. and another study [153] evaluates YOLOv7, Faster R-CNN, SSD, and HoG for both vehicle and pedestrian detection in different weather conditions. YOLOv7's superior performance in terms of accuracy, precision, recall, and mAP underscores the critical role of weather conditions in selecting appropriate DL methods for autonomous driving.…”

Section: Approaches For Pedestrian Detectionmentioning

confidence: 99%

“…Montenegro et al [152] have shown that YOLO-v5 can be fine-tuned to maintain high detection accuracy across varying lighting conditions, ensuring consistent performance for AVs. Zaman et al [153] have addressed the challenge of adverse weather conditions, a common obstacle for AVs, by developing DL models that can adapt to weather-induced distortions, thereby improving system reliability. These studies have identified key performance indicators (KPIs) such as detection accuracy, false negative rate, computational efficiency, and real-time processing capabilities, which are essential for evaluating the practicality of DL models in AVs, as shown in Table 6.…”

Section: Practical Implicationsmentioning

confidence: 99%

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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: Approaches For Pedestrian Detectionmentioning

confidence: 99%

Section: Practical Implicationsmentioning

confidence: 99%