@article{Adamski_Lubczonek_2025,
	author = {Adamski, Pawel and Lubczonek, Jacek},
	title = {Automatic Detection of Navigational Signs on Inland Waterways Using YOLO Neural Networks},
	journal = {TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation},
	volume = {19},
	number = {4},
	pages = {1203-1208},
	year = {2025},
	url = {./Article_Automatic_Detection_of_Navigational_Adamski,76,1608.html},
	abstract = {The study analysed the detection of navigation signs for inland navigation using YOLO neural networks. All major versions of the network available at the time of the study were analysed, i.e. from YOLO 1 to YOLO 12. The study considered two criteria: detection efficiency and detection accuracy. The first case is related to applications requiring the highest possible number of object detections, while the second is related to mapping tasks, where the accuracy of determining the location of a sign plays an important role. The results of the study showed that different efficiencies can be expected for the neural models studied. The latest models do not always prove to be the best. In terms of detection efficiency, the YOLO 4 network proved to be the best model, while in terms of sign detection precision, YOLO v7 had the highest horizontal accuracy and YOLO v10 had the highest vertical accuracy. The results of the study indicated that in some cases, attention should be paid to the oldest versions of YOLO, such as YOLO V1 and V2, which significantly reduce false detections.},
	doi = {10.12716/1001.19.04.18},
	issn = {2083-6473},
	publisher = {Gdynia Maritime University, Faculty of Navigation},
	keywords = {}
}