Teat detection algorithm: YOLO vs. Haar-cascade

In this study we have developed and experimented with two methods of teat detection based on machine learning approach in image recognition and object detection. Automatic milking systems rely strongly on the vision system for successful milking operation initiation which is the attachment of the te...

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Vydané v:Journal of mechanical science and technology Ročník 33; číslo 4; s. 1869 - 1874
Hlavní autori: Rastogi, Akanksha, Ryuh, Beom Sahng
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Seoul Korean Society of Mechanical Engineers 01.04.2019
Springer Nature B.V
대한기계학회
Predmet:
Algorithms
Control
Dynamical Systems
Edge detection
Engineering
Image processing
Indoor environments
Industrial and Production Engineering
Machine learning
Machine vision
Mechanical Engineering
Neural networks
Object recognition
Vibration
Vision systems
기계공학
YOLO
Automatic milking systems
Haar-cascade
Teat detection
ISSN:1738-494X, 1976-3824
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Popis
Shrnutí:In this study we have developed and experimented with two methods of teat detection based on machine learning approach in image recognition and object detection. Automatic milking systems rely strongly on the vision system for successful milking operation initiation which is the attachment of the teat cups correctly. Teat detection method currently employed in the industry is based on laser assisted edge detection mechanism, making the current systems less advanced than the existing methods in the field of image processing and robotic vision. By experimenting on a basic object detection method based on Haar-like features, viz. Haar cascade classification method and a latest state-of-the-art method based on convolutional neural nets, viz. YOLO-object detection method, we have compared the results of detection on a fake teat model casted from silicon, especially for indoor environments. This study is in extension to the successful real time detection in a cow farm using Haar-cascade based algorithm.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-019-0339-5