VDNet: Vehicle Detection Network Using Computer Vision and Deep Learning Mechanism for Intelligent Vehicle System

Ojha, Apoorva; Sahu, Satya Prakash; Dewangan, Deepak Kumar

doi:10.1007/978-981-16-3097-2_9

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…Another paper [23] was written by A. Ojha et al, who proposed a hybrid intelligent model for real-time vehicle detection and tracking. The proposed model consists of a YOLOv3 object detector, a CNN-based tracker, and a Kalman filter (KF) estimator.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Khan

Lee²,

Lim

2023

Electronics

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Recent advancements in artificial intelligence (AI) have greatly improved the object detection capabilities of autonomous vehicles, especially using convolutional neural networks (CNNs). However, achieving high levels of accuracy and speed simultaneously in vehicular environments remains a challenge. Therefore, this paper proposes a hybrid approach that incorporates the features of two state-of-the-art object detection models: You Only Look Once (YOLO) and Faster Region CNN (Faster R-CNN). The proposed hybrid approach combines the detection and boundary box selection capabilities of YOLO with the region of interest (RoI) pooling from Faster R-CNN, resulting in improved segmentation and classification accuracy. Furthermore, we skip the Region Proposal Network (RPN) from the Faster R-CNN architecture to optimize processing time. The hybrid model is trained on a local dataset of 10,000 labeled traffic images collected during driving scenarios, further enhancing its accuracy. The results demonstrate that our proposed hybrid approach outperforms existing state-of-the-art models, providing both high accuracy and practical real-time object detection for autonomous vehicles. It is observed that the proposed hybrid model achieves a significant increase in accuracy, with improvements ranging from 5 to 7 percent compared to the standalone YOLO models. The findings of this research have practical implications for the integration of AI technologies in autonomous driving systems.

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Section: Related Workmentioning

confidence: 99%

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Khan

Lee²,

Lim

2023

Electronics

View full text Add to dashboard Cite

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“…As a result, numerous computer vision and machine learning models are thoroughly investigated in this study in order to solve a range of fascinating challenges in intelligent transportation systems. Researchers have suggested various classic vehicle detection algorithms from the earliest stages of the field to current days [9][10][11][12][13][14][15]. The performance of techniques is determined by handcrafted characteristics.…”

Section: Introductionmentioning

confidence: 99%

Faster RCNN based robust vehicle detection algorithm for identifying and classifying vehicles

Alam,

Ahmed,

Salih

et al. 2023

J Real-Time Image Proc

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Deep convolutional neural networks (CNNs) have shown tremendous success in the detection of objects and vehicles in recent years. However, when using CNNs to identify real-time vehicle detection in a moving context remains difficult. Many obscured and truncated cars, as well as huge vehicle scale fluctuations in traffic photos, provide these issues. To improve the performance of detection findings, we used multiscale feature maps from CNN or input pictures with numerous resolutions to adapt the base network to match different scales. This research presents an enhanced framework depending on Faster R-CNN for rapid vehicle recognition which presents better accuracy and fast processing time. Research results on our custom dataset indicate that our recommended methodology performed better in terms of detection efficiency and processing time, especially in comparison to the earlier age of Faster R-CNN models.

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Machine Learning Empowered IoT Devices, Analysis of Indoor and Outdoor Temperature and Health Risks

Sohail,

Ahmad

2024

Smart Sensors, Measurement and Instrumentation

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VDNet: Vehicle Detection Network Using Computer Vision and Deep Learning Mechanism for Intelligent Vehicle System

Cited by 9 publications

References 29 publications

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Enhancing Object Detection in Self-Driving Cars Using a Hybrid Approach

Faster RCNN based robust vehicle detection algorithm for identifying and classifying vehicles

Machine Learning Empowered IoT Devices, Analysis of Indoor and Outdoor Temperature and Health Risks

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