August 4, 2025

Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm

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MAIN FINDING: The YOLOv8-ECSH algorithm improves pedestrian and vehicle detection at night.
KEY EVIDENCE: The model shows a 4.1% increase in recall and a 1.8% increase in mAP to 92.6%.
APPROACH: Utilizes CARAFE for up-sampling, replaces CIOU Loss with EIOU Loss, and adds detection layers.
SIGNIFICANCE: Enhancements lead to better accuracy and speed in detecting small targets under challenging nighttime conditions.

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Implication

Novel YOLOv8-ECSH enhances detection speed and accuracy for small targets in nighttime environments, suggesting effective solutions for complex conditions.

Cite This Study

Zou et al. (2025) studied this question.

synapsesocial.com/papers/689a0f8de6551bb0af8d0dbf https://doi.org/10.21203/rs.3.rs-4495135/v1

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Implication

Novel YOLOv8-ECSH enhances detection speed and accuracy for small targets in nighttime environments, suggesting effective solutions for complex conditions.

Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm

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Discussion

Implication

Cite This Study

Object Detection of Pedestrain and Vehicle at Night Based on Improved YOLO Algorithm

Key Points

Discussion

Implication

Cite This Study

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