Baza Publikacji Pracowników Politechniki Lubelskiej

Status:
Autorzy:	Tomiło Paweł
Dyscypliny:
	Aby zobaczyć szczegóły należy się zalogować.
Rok wydania:	2025
Wersja dokumentu:	Elektroniczna
Język:	angielski
Numer czasopisma:	1
Wolumen/Tom:	21
Strony:	164 - 181
Efekt badań statutowych	NIE
Finansowanie:	This research was funded in whole or in part by National Science Centre, Poland 2024/08/X/ST6/00610. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.
Materiał konferencyjny:	NIE
Publikacja OA:	TAK
Licencja:
Sposób udostępnienia:	Otwarte czasopismo
Wersja tekstu:	Ostateczna wersja opublikowana
Czas opublikowania:	W momencie opublikowania
Data opublikowania w OA:	6 stycznia 2025
Abstrakty:	angielski
	Poor pavement condition leads to increased risk of accidents, vehicle damage, and reduced transportation efficiency. The author points out that traditional methods of monitoring road conditions are time- consuming and costly, so a modern approach based on the use of developed neural network model is presented. The main aim of this paper is to create a model that can infer in real time, with less computing power and maintaining or improving the metrics of the base model, YOLOv8. Based on this assumption, the architecture of the LANA-YOLOv8 (Large Kernel Attention Involution Asymptotic Feature Pyramid) is proposed. The model's architecture is tailored to operate in environments with limited resources, including single-board minicomputers. In addition, the article presents Basic Involution Block (BIB) that uses the involution layer to provide better performance at a lower cost than convolution layers. The model was compared with other architectures on a public dataset as well as on a dataset specially created for these purposes. The developed solution has lower computing power requirements, which translates into faster inference times. At the same time, the developed model achieved better results in validation tests against the base model.

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LANA-YOLO: Road defect detection algorithm optimized for embedded solutions

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