A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions

Ghosh, Rajib

doi:10.1007/978-981-16-1295-4_5

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Additionally, Ref. [136] proposes a modified YOLO model for on-road vehicle detection and tracking across various weather conditions. Utilizing a single CNN, it exhibits robustness and outperforms the existing techniques in intelligent transportation systems.…”

Section: Approaches For Vehicle Detectionmentioning

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: Approaches For Vehicle Detectionmentioning

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

View full text Add to dashboard Cite

show abstract

“…A common example of this technique is binarization, where the image is segmented into two distinct sets of pixels, often used to clearly separate objects from the background. The main objective of image segmentation in our study is to extract significant objects present in images (Ghosh, 2021). This involves dividing images into semantic regions, making it easier to identify and count vehicles.…”

Section: Image Segmentationmentioning

confidence: 99%

Integration of Deep Learning Models for Vehicle Counting: Towards Optimized Planning of Urban Charging Infrastructures

Benayad,

Rochd,

Houran

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Amidst the rising prevalence of electric vehicles (EV) and the corresponding need for adequate charging infrastructures, this study presents an innovative application of deep learning models to analyze urban traffic. Utilizing the YOLO v3 model, our research focuses on developing a prototype for counting vehicles at an urban roundabout connecting three main roads, providing essential data for planning EV charging infrastructures. This work involves adapting and optimizing the YOLO v3 model to accurately identify and count vehicles passing through the roundabout. The aim is to measure traffic volume and identify circulation trends, which are crucial for understanding the spatial and temporal distribution of vehicles. The collected data not only estimate the number of EVs likely to traverse this area but also predict the needs for charging infrastructure in and around this specific roundabout. Our prototype demonstrated high accuracy in vehicle counting, allowing detailed analysis of traffic patterns at different times of the day. This analysis reveals significant variations in traffic flows, providing valuable insights into the most suitable times and locations for installing EV charging stations. While this study focuses on a single roundabout, it establishes a precedent for using advanced deep learning techniques in assessing EV charging infrastructure needs in broader urban contexts. The results suggest that such methods can be extended to other urban areas for more comprehensive and effective planning of EV charging infrastructures, thus contributing to more sustainable and intelligent urban mobility management.

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Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges

Chamola,

Chougule,

Sam

et al. 2024

IEEE Internet Things J.

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A Modified YOLO Model for On-Road Vehicle Detection in Varying Weather Conditions

Cited by 4 publications

References 21 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

Integration of Deep Learning Models for Vehicle Counting: Towards Optimized Planning of Urban Charging Infrastructures

Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges

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