YOLOv5 Deep Neural Network for Quince and Raspberry Detection on RGB Images

• Published in: 2022 Workshop on Microwave Theory and Techniques in Wireless Communications (MTTW) pp. 19 - 22
• Main Authors: Sudars, Kaspars, Namatevs, Ivars, Judvaitis, Janis, Balass, Rihards, Nikulins, Arturs, Peter, Astile, Strautina, Sarmite, Kaufmane, Edite, Kalnina, Ieva
• Format: Conference Proceeding
• Language: English
• Published: IEEE 05.10.2022
• Subjects: Deep learning; Deep neural networks; Image processing; Neural networks; Point cloud compression; Precise agriculture; Three-dimensional displays; Training; Wireless communication; Yield estimation; YOLOv5

Object detection based on deep learning can be widely used in all kinds of agricultural applications. In this paper, we present a deep neural network (DNN) model for quince and raspberry detection on RGB images. The trained DNN model is based on YOLOv5 architecture and it has 7 berry classes related...