Model generalization experiment.

<div><p>Road crack detection is critical to global infrastructure maintenance and public safety, and complex background environments and nonlinear damage crack patterns challenge the need for real-time, efficient, and accurate detection.This paper proposes a lightweight yet robust Flexi-...

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Bibliographic Details
Main Author:	Jiexiang Yang (19980594) (author)
Other Authors:	Renjie Tian (21553556) (author), Zexing Zhou (21553559) (author), Xingyue Tan (21553562) (author), Pingyang He (21553565) (author)
Published:	2025
Subjects:	Ecology Space Science Biological Sciences not elsewhere classified Information Systems not elsewhere classified recall rate rise model &# 8217 lower application costs experimental results show complex shape characteristics complex background environments akconv convolution operation global infrastructure maintenance f1 score improvement lightweight network design road crack detection global attention module yolo model based 5 &# 8211 global information c2f module lightweight method automated detection accurate detection yolov8 algorithm yolo achieves regression accuracy quality samples public safety precise solution paper proposes map improvement loss function industrial demands feature redundancy feature information establishing g designed wise cracks dynamically convolutional kernels bounding boxes adaptively adjust accuracy increase 95 increase 9 %,
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Model generalization experiment.

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