Road defect detection based on improved YOLOv8s model

Bibliographic Details
Title:	Road defect detection based on improved YOLOv8s model
Authors:	Jinlei Wang, Ruifeng Meng, Yuanhao Huang, Lin Zhou, Lujia Huo, Zhi Qiao, Changchang Niu
Source:	Scientific Reports, Vol 14, Iss 1, Pp 1-21 (2024)
Publisher Information:	Nature Portfolio, 2024.
Publication Year:	2024
Collection:	LCC:Medicine LCC:Science
Subject Terms:	Medicine, Science
More Details:	Abstract Road defect detection is critical step for road maintenance periodic inspection. Current methodologies exhibit drawbacks such as low detection accuracy, slow detection speed, and the inability to support edge deployment and real-time detection. To solve this issue, we introduce an improved YOLOv8 road defect detection model. Firstly, we designed the EMA Faster Block structure using partial convolution to replace the Bottleneck structure in the YOLOv8 C2f module, and the enhanced C2f module was labeled as C2f-Faster-EMA. Secondly, we improved the model speed by introducing SimSPPF instead of SPPF. Finally, for the head, Detect-Dyhead, chosen to replace the original head, significantly improves the representation ability of heads without introducing any GFLOPs. Experimental results on the road defect detection dataset show that the improved model in this paper outperforms the original YOLOv8, with a 5.8% increase in average accuracy (mAP@0.5), and notable reductions of 22.33% in model size, 23.03% in parameter size, and 21.68% in computational complexity.
Document Type:	article
File Description:	electronic resource
Language:	English
ISSN:	2045-2322
Relation:	https://doaj.org/toc/2045-2322
DOI:	10.1038/s41598-024-67953-3
Access URL:	https://doaj.org/article/80a140fd6f7a40cb9f5ef8eb2044c4ae
Accession Number:	edsdoj.80a140fd6f7a40cb9f5ef8eb2044c4ae
Database:	Directory of Open Access Journals
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More Details
ISSN:	20452322
DOI:	10.1038/s41598-024-67953-3
Published in:	Scientific Reports
Language:	English