What type of study is this?

This is a In Vitro Study study.

August 25, 2025World Electric Vehicle JournalOpen Access

YOLOv5s-F: An Improved Algorithm for Real-Time Monitoring of Small Targets on Highways

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Authors

GJGuo JinhaoJiangsu University GGGuoqing GengJiangsu University SLSun LiqinJiangsu University

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Implication

Algorithm YOLOv5s-F improves small target detection speed and accuracy in highway monitoring, highlighting innovations in object detection technology.

Key Points

YOLOv5s-F improves real-time detection speed and accuracy for small objects by 15.4% and 2.1%, respectively, addressing monitoring challenges.
Using the FasterNet structure, it decreases computational complexity and parameter count by 10.1% and 24.6% compared to the YOLOv5s baseline.
Incorporating a small-object detection layer and Focal EIoU significantly enhances localization accuracy and model training convergence.
The algorithm achieves 32 FPS on NVIDIA Jetson Xavier NX, successfully meeting real-time requirements for in-vehicle monitoring.

Cite This Study

Jinhao et al. (2025) studied this question.

synapsesocial.com/papers/68af5d75ad7bf08b1eae147a https://doi.org/10.3390/wevj16090483

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Implication

Algorithm YOLOv5s-F improves small target detection speed and accuracy in highway monitoring, highlighting innovations in object detection technology.

YOLOv5s-F: An Improved Algorithm for Real-Time Monitoring of Small Targets on Highways

Discussion

Member takes

Implication

Key Points

Cite This Study

YOLOv5s-F: An Improved Algorithm for Real-Time Monitoring of Small Targets on Highways

Discussion

Member takes

Implication

Key Points

Cite This Study

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