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.

Understanding the combustion behavior of electric bicycle batteries and unveiling its relationship with fire extinguishing

In this study,a detailed analysis of the combustion behaviors of the lithium iron phosphate(LFP)and lithium manganese oxide(LMO)batteries used in electric bicycles was conducted.This research included quantitative measurements of the combustion duration,flame height,combustion temperature,heat release rate,and total heat release.The results indicated that LMO batteries exhibited higher combustion temperatures of 600–700°C,flame heights of 70–75 cm,a significantly higher heat release rate of40.1 k W(12 Ah),and a total heat release of 1.04 MJ(12 Ah)compared to LFP batteries with the same capacity.Based on these experimental results,a normalized total heat release(NORTHR)parameter was proposed,demonstrating good universality for batteries with different capacities.Utilizing this parameter,quantitative calculations and optimization of the extinguishing agent dosage were conducted for fires involving these two types of batteries,and the method was validated by extinguishing fires for these two types of battery packs with water-based extinguishing fluids.

Research on Planning and Design of Multi-Story Prefabricated Electric Bicycle Charging Parking Lot in Urban and Rural Areas

In recent years,disorderly parking and difficult charging of electric bicycles have been challenges in urban management.The rapid growth of electric bicycles is in contradiction with the lack of dedicated parking spaces and charging service facilities in towns and villages.To solve the issue of parking and charging electric bicycles in limited urban and rural spaces,prefabricated building technology is applied to the design of a multi-story electric bicycle parking lot.The multi-story prefabricated electric bicycle parking lot is utilized in urban and rural planning and design to upgrade parking facilities in old urban areas,land-constrained commercial areas,as well as counties,towns,and rural areas with inadequate municipal facilities.Multi-story prefabricated electric bicycle parking lots are the application exploration of industrial buildings,and promote the high-quality development planning and construction of towns and counties and villages.Compared with the single-story metal charging station,the multi-story assembled electric bicycle parking lot has the characteristics of integrating parking and charging,being more durable and safer in structure,accommodating a large number of vehicles,and improving the space utilization rate.

Influencing factors analysis of helmet wearing for electric bicycle riders based on ordinal multinomial logistic model

The helmet of riders of electric bicycles plays an important role in reducing injuries and deaths in traffic accidents.This paper conducts a questionnaire survey,data analysis and modelling to investigate the influencing factors of electric bicycle helmet wearing.First,living area,gender,age,marital status and education level are selected as independent variables for data analysis.The factor analysis divides the sentiments of electric bicyclists for wearing helmets into four aspects:safety perception,practical sensation,satisfaction perception and emergency perception,and ordinal multiple logistic models are built to analyse the influencing factors.The result shows that people aged 18−25,26−35,36−45 and 46−55 are 1.3,1.8,2.0 and 2.3 times more likely,respectively,to have at least a grade higher safety perception than those aged 56 and over;men are 0.77 times more likely than women to feel at least one grade higher about the practical perception and 1.48 times more than women about the satisfaction perception;people with primary school,junior high school,senior high school,junior college and bachelor’s degree education are 1.64,2.44,1.50,1.70 and 1.55 times more likely,respectively,than people with a master’s degree to feel at least one grade higher about the satisfaction perception.