Improved YOLOv8n Model for Detecting Helmets and License Plates on Electric Bicycles

Wearing helmetswhile riding electric bicycles can significantly reduce head injuries resulting fromtraffic accidents.To effectively monitor compliance,the utilization of target detection algorithms through traffic cameras plays a vital role in identifying helmet usage by electric bicycle riders and recognizing license plates on electric bicycles.However,manual enforcement by traffic police is time-consuming and labor-intensive.Traditional methods face challenges in accurately identifying small targets such as helmets and license plates using deep learning techniques.This paper proposes an enhanced model for detecting helmets and license plates on electric bicycles,addressing these challenges.The proposedmodel improves uponYOLOv8n by deepening the network structure,incorporating weighted connections,and introducing lightweight convolutional modules.These modifications aim to enhance the precision of small target recognition while reducing the model’s parameters,making it suitable for deployment on low-performance devices in real traffic scenarios.Experimental results demonstrate that the model achieves an mAP@0.5 of 91.8%,showing an 11.5%improvement over the baselinemodel,with a 16.2%reduction in parameters.Additionally,themodel achieves a frames per second(FPS)rate of 58,meeting the accuracy and speed requirements for detection in actual traffic scenarios.

Structure Analysis and Classification of Precipitation Cells by Fractal Geometry

This paper analyzes the possibility to discriminate between convective precipitation and stratiform precipitation. This study aims to improve the measurement of rainfall from teledetection data obtained both on the ground and in space. For this, two parameters, fractal dimension and fractal lacunarity, are considered. To calculate the fractal dimension, we use the approach of box-counting and show that the fractal dimension differs between convectives cells and stratiforms ones. And then the fractal lacunarity parameter is calculated by using the sliding boxes algorithm. The study for all the regions shows that precipitation cells can be described by different lacunarities whatever the scale of analysis. We deduce that the two parameters, fractal dimension and fractal lacunarity, can be used to classify precipitations in convective regime and stratiform regime.

Longitudinal analysis of quantitative biomarkers using projection-resolved OCT angiography in retinal vein occlusion

Background:To evaluate a fully automated vascular density(VD),skeletal density(SD)and fractal dimension(FD)method for the longitudinal analysis of retinal vein occlusion(RVO)eyes using projection-resolved optical coherence tomography angiography(OCTA)images and to evaluate the association between these quantitative variables and the visual prognosis in RVO eyes.Methods:Retrospective longitudinal observational case series.Patients presenting with RVO to Creteil University Eye Clinic between October 2014 and December 2018 and healthy controls were retrospectively evaluated.Group 1 consisted of central RVO(CRVO)eyes,group 2 consisted of eyes with branch RVO(BRVO)and group 3 of healthy control eyes.OCTA acquisitions(AngioVue RTVue XR Avanti,Optovue,Inc.,Freemont,CA)were performed at baseline and last follow up visit.VD,SD,and FD analysis were computed on OCTA superficial and deep vascular complex(SVC,DVC)images at baseline and final follow up using an automated algorithm.Logistic regression was performed to find if and which variable(VD,SD,FD)was predictive for the visual outcome.Results:Forty-one eyes,of which 21 consecutive eyes of 20 RVO patients(13 CRVO in group 1,8 BRVO in group 2),and 20 eyes of 20 healthy controls were included.At the level of SVC,VD and FD were significantly lower in RVO eyes compared to controls(P<0.0001 and P=0.0008 respectively).Best-corrected visual acuity(BCVA)at last follow-up visit was associated with baseline VD(P=0.013),FD(P=0.016),and SD(P=0.01)at the level of the SVC,as well as with baseline FD at the DVC level(P=0.046).Conclusions:Baseline VD,SD,and FD are associated with the visual outcome in RVO eyes.These parameters seem valuable biomarkers and may help improve the evaluation and management of RVO patients.