Two-Wheeled Vehicle Detection Using Two-Step and Single-Step Deep Learning Models

<span lang="EN-US">The present research focuses on developing an intelligent traffic management solution for tracking the vehicles on roads. Our proposed work focuses on a much better you only look once (YOLOv4) traffic monitoring system that uses the CSPDarknet53 architecture as its foundation. Deep-sort learning methodology for vehicle multi-target detection from traffic video is also part of our research study. We have included features like the Kalman filter, which estimates unknown objects and can track moving targets. Hungarian techniques identify the correct frame for the object. We are using enhanced object detection network design and new data augmentation techniques with YOLOv4, which ultimately aids in traffic monitoring. Until recently, object identification models could either perform quickly or draw conclusions quickly. This was a big improvement, as YOLOv4 has an astoundingly good performance for a very high frames per second (FPS). The current study is focused on developing an intelligent video surveillance-based vehicle tracking system that tracks the vehicles using a neural network, image-based tracking, and YOLOv4. Real video sequences of road traffic are used to test the effectiveness of the method that has been suggested in the research. Through simulations, it is demonstrated that the suggested technique significantly increases graphics processing unit (GPU) speed and FSP as compared to baseline algorithms.</span>

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“…Figure 2(b). Show the flow the vehicle detection method, which is in flow with multiple object tracking [24].…”

Section: Methodsmentioning

confidence: 99%

Application of improved you only look once model in road traffic monitoring system

Kamble

Kounte

2023

IJECE

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“…Despite achieving high accuracy, reaching 90.8% and 88.6% for low and high difficulty sets, respectively, the computational costs are considerable, prioritizing detection robustness over real-time applicability. In [21], Faster R-CNN with Inception-ResNet backbone performs well, with the single-stage SSD network with Inception outperforming others in accuracy with preprocessing techniques. However, real-time applications are not considered, posing challenges for embedded solutions.…”

Section: Related Workmentioning

confidence: 98%

“…Motorbike detection is an object detection task with several models available for this purpose, as pointed out earlier. Paper [21] reports several experiments with over 50 networks to identify the best model for motorbike detection and concludes that SSD with a Resnet backbone works best for this application. This work also uses the SSD architecture, but its Resnet backbone is replaced with Mobilenet V2.…”

Section: Model Selectionmentioning

confidence: 99%

Real-Time Motorbike Detection: AI on the Edge Perspective

Akhtar,

Ahmed,

Yousaf

et al. 2024

Mathematics

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Motorbikes are an integral part of transportation in emerging countries, but unfortunately, motorbike users are also one the most vulnerable road users (VRUs) and are engaged in a large number of yearly accidents. So, motorbike detection is very important for proper traffic surveillance, road safety, and security. Most of the work related to bike detection has been carried out to improve accuracy. If this task is not performed in real-time then it loses practical significance, but little to none has been reported for its real-time implementation. In this work, we have looked at multiple real-time deployable cost-efficient solutions for motorbike detection using various state-of-the-art embedded edge devices. This paper discusses an investigation of a proposed methodology on five different embedded devices that include Jetson Nano, Jetson TX2, Jetson Xavier, Intel Compute Stick, and Coral Dev Board. Running the highly compute-intensive object detection model on edge devices (in real-time) is made possible by optimization. As a result, we have achieved inference rates on different devices that are twice as high as GPUs, with only a marginal drop in accuracy. Secondly, the baseline accuracy of motorbike detection has been improved by developing a custom network based on YoloV5 by introducing sparsity and depth reduction. Dataset augmentation has been applied at both image and object levels to enhance robustness of detection. We have achieved 99% accuracy as compared to the previously reported 97% accuracy, with better FPS. Additionally, we have provided a performance comparison of motorbike detection on the different embedded edge devices, for practical implementation.

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“…The algorithms are employed to detect and classify object classes from images and videos. Kausa et al [ 56 ] utilized both single and two-step object detector approaches for two-wheeled and four-wheeled vehicle detection from publicly available datasets. Vasavi et al [ 57 ] also applied integrated YOLO and RCNN algorithms for vehicle detection and classification from high-resolution images.…”

Section: Deep Learning Techniquesmentioning

confidence: 99%

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

Berwo

Khan

Fang

et al. 2023

Sensors

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Detecting and classifying vehicles as objects from images and videos is challenging in appearance-based representation, yet plays a significant role in the substantial real-time applications of Intelligent Transportation Systems (ITSs). The rapid development of Deep Learning (DL) has resulted in the computer-vision community demanding efficient, robust, and outstanding services to be built in various fields. This paper covers a wide range of vehicle detection and classification approaches and the application of these in estimating traffic density, real-time targets, toll management and other areas using DL architectures. Moreover, the paper also presents a detailed analysis of DL techniques, benchmark datasets, and preliminaries. A survey of some vital detection and classification applications, namely, vehicle detection and classification and performance, is conducted, with a detailed investigation of the challenges faced. The paper also addresses the promising technological advancements of the last few years.

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Two-Wheeled Vehicle Detection Using Two-Step and Single-Step Deep Learning Models

Cited by 10 publications

References 42 publications

Application of improved you only look once model in road traffic monitoring system

Application of improved you only look once model in road traffic monitoring system

Real-Time Motorbike Detection: AI on the Edge Perspective

Deep Learning Techniques for Vehicle Detection and Classification from Images/Videos: A Survey

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