Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

Sharma, Nabin; Baral, Sushish; Paing, May Phu; Chawuthai, Rathachai

doi:10.3390/s23135843

Cited by 34 publications

(6 citation statements)

References 35 publications

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“…Built upon the accomplishments of its predecessors, YOLOv8 stands as a cutting-edge model that introduces fresh features and enhancements to bolster performance and flexibility ( Sharma et al, 2023 ). Notable innovations comprise a novel backbone network, an advanced Anchor-Free detection header, and a fresh loss function capable of operating across a range of hardware platforms, spanning CPUs to GPUs.…”

Section: Methodsmentioning

confidence: 99%

An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network

Yu,

Wan,

Yousaf

et al. 2024

Poultry Science

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Section: Methodsmentioning

confidence: 99%

An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network

Yu,

Wan,

Yousaf

et al. 2024

Poultry Science

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“…CSPDarknet53 is an advanced neural network architecture that focuses on efficient feature extraction from input images [17]. It employs the Cross Stage Partial (CSP) design to balance detail and efficiency, improving the network's ability to capture important visual details and broader scene context [18,19]. The model includes a Spatial Pyramid Pooling (SPP) Fast layer to improve detection accuracy and speed by optimizing feature extraction [16].…”

Section: Yolov8 Architecture and Featuresmentioning

confidence: 99%

Improved accuracy for estrous cycle staging using supervised object detection

Babaev,

Goyal,

Ross

2024

Preprint

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The estrous cycle regulates reproductive events and hormone changes in female mammals and is analogous to the menstrual cycle in humans. Monitoring this cycle is necessary as it serves as a biomarker for overall health and is crucial for interpreting study results. The estrous cycle comprises four stages influenced by fluctuating levels of hormones, mainly estradiol and progesterone. Tracking the cycle traditionally relies on vaginal cytology, which categorizes stages based on three epithelial cell concentrations. However, this method has limitations, including time-consuming training and variable accuracy among researchers. To address these challenges, this study assessed the feasibility and reliability of two machine learning methods. An object detection-based machine learning model, Object Detection Estrous Staging (ODES), was employed to identify cell types throughout the estrous cycle in mice. A dataset of 555 vaginal cytology images with four different stains was annotated, with 335 images for training, 45 for validation, and 175 for testing. A novel, accurate set of rules for classification was derived by analyzing training images. ODES achieved an average accuracy of 87% in classifying cycle stages and took only 3.9 minutes to analyze 175 test images. The use of object detection machine learning significantly improved accuracy and efficiency compared to previously derived supervised image classification models (33-45% accuracy) and human accuracy (66% accuracy), refining research practices for female studies. These findings facilitate the integration of the estrous cycle into research, enhancing the quality of scientific results by allowing for efficient and accurate identification of the cycle stage.

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“…Bounding boxes in the form of annotations are widely prevalent in the field of deep learning, surpassing other types of annotations in terms of frequency [29]. Within the domain of computer vision, the term "bounding boxes" refers to rectangular shapes utilized to delineate and specify the precise spatial coordinates of the object under scrutiny.…”

Section: Yolov8 Architecturementioning

confidence: 99%

Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System

Dewi,

Manongga,

Hendry

et al. 2024

BDCC

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Face mask detection is a technological application that employs computer vision methodologies to ascertain the presence or absence of a face mask on an individual depicted in an image or video. This technology gained significant attention and adoption during the COVID-19 pandemic, as wearing face masks became an important measure to prevent the spread of the virus. Face mask detection helps to enforce mask-wearing guidelines, which can significantly reduce the spread of respiratory illnesses, including COVID-19. Wearing masks in densely populated areas provides individuals with protection and hinders the spread of airborne particles that transmit viruses. The application of deep learning models in object recognition has shown significant progress, leading to promising outcomes in the identification and localization of objects within images. The primary aim of this study is to annotate and classify face mask entities depicted in authentic images. To mitigate the spread of COVID-19 within public settings, individuals can employ the use of face masks created from materials specifically designed for medical purposes. This study utilizes YOLOv8, a state-of-the-art object detection algorithm, to accurately detect and identify face masks. To analyze this study, we conducted an experiment in which we combined the Face Mask Dataset (FMD) and the Medical Mask Dataset (MMD) into a single dataset. The detection performance of an earlier research study using the FMD and MMD was improved by the suggested model to a “Good” level of 99.1%, up from 98.6%. Our study demonstrates that the model scheme we have provided is a reliable method for detecting faces that are obscured by medical masks. Additionally, after the completion of the study, a comparative analysis was conducted to examine the findings in conjunction with those of related research. The proposed detector demonstrated superior performance compared to previous research in terms of both accuracy and precision.

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Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms

Cited by 34 publications

References 35 publications

An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network

An enhancement algorithm for head characteristics of caged chickens detection based on cyclic consistent migration neural network

Improved accuracy for estrous cycle staging using supervised object detection

Deep Learning and YOLOv8 Utilized in an Accurate Face Mask Detection System

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