Improved Field Obstacle Detection Algorithm Based on YOLOv8

To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast dete...

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Veröffentlicht in:	Agriculture (Basel) Jg. 14; H. 12; S. 2263
Hauptverfasser:	Zhou, Xinying, Chen, Wenming, Wei, Xinhua
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.12.2024
Schlagworte:	Accuracy Agricultural equipment Agricultural land Agricultural technology Agricultural vehicles Agriculture Algorithms BiFPN Boxes CBAM Computer vision Datasets Deep learning Detectors Electric power Farm machinery Farmers field obstacle detection Fieldwork Frames per second Harvesters Image quality Lighting Obstacle avoidance Pylons Real time Sensors Target detection Task complexity Tractors Vision systems WIoU v3 YOLOv8
ISSN:	2077-0472, 2077-0472
Online-Zugang:	Volltext
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