MA-YOLOv8: Road Vehicle Target Detection Based on MBConv Module and C2f_AKConv Structure Improved YOLOv8

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The improved model increases average accuracy from 0.352 to 0.466, enhancing overall detection performance.
A recall rate improvement from 0.421 to 0.539 significantly reduces missed detections in traffic scenarios.
Incorporating the mbconv module and c2f_akconv structure optimizes feature extraction and scale adaptability.
The model shows notable advancements in detecting vehicles with complex shapes and small targets.

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The improved yolo model increases detection accuracy and recall rates in road vehicle categories, highlighting effective feature extraction.

Ting Zhang (2025) studied this question.

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The improved yolo model increases detection accuracy and recall rates in road vehicle categories, highlighting effective feature extraction.