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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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License plate recognition methodology in complex scenarios based on CSCM-YOLOv8 and CSM-LPRNet.

Weihua Xiong¹, Lixian Cao¹, Dongming Yan¹

¹School of Information and Control Engineering, Jilin University of Chemical Technology, Jilin, China.

|January 2, 2026

Summary

This summary is machine-generated.

This study introduces an improved license plate recognition (LPR) method using CSCM-YOLOv8 and CSM-LPRNet. The enhanced system achieves higher accuracy in complex conditions, offering a robust solution for intelligent transportation systems.

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Area of Science:

Computer Vision
Artificial Intelligence
Intelligent Transportation Systems

Background:

License plate recognition (LPR) is crucial for traffic management but struggles in complex environments (e.g., poor lighting, adverse weather).
Existing LPR systems require enhanced accuracy and robustness for reliable real-world application.

Purpose of the Study:

To develop a robust license plate recognition method for challenging environmental conditions.
To improve both the detection and character recognition accuracy of LPR systems.

Main Methods:

Proposed a novel LPR method combining CSCM-YOLOv8 for detection and CSM-LPRNet for recognition.
Integrated CPA-Enhancer for input optimization and CARAFE for improved upsampling.
Employed SEAM with C2fMLLABlock for efficient feature extraction and aggregation.

Main Results:

CSCM-YOLOv8 achieved 98.9% detection accuracy and 58.0% mAP@0.50-0.95, outperforming original YOLOv8 by 3.1% and 3.9%.
CSM-LPRNet reached 98.56% character recognition accuracy, a 7.0% improvement over LPRNet.
The method demonstrated significant performance gains in complex scenarios.

Conclusions:

The proposed CSCM-YOLOv8 and CSM-LPRNet method offers a reliable and efficient solution for LPR in complex environments.
This advancement contributes to more effective intelligent transportation systems.
The integrated modules enhance feature extraction and reduce computational cost effectively.