Abstract
As the global aging intensifies, it is more convenient for a robot to go for buying things like fruits and vegetables instead of elderly, and it is more human-like to select items according to a user’s personal preferences such as. maturity of fruits, sweetness, etc. However, Fruits and vegetables are generally displayed in a disorderly manner. Therefore, detection and recognition of fruits and vegetables is a difficult task for a robot. This paper proposes an improved YOLOv3 and also pre-training the networks to detect fruits and vegetables,we then using Bilinear-CNN to classifyfruit’s maturity. The effectiveness of the proposed method is shown by experiments.
Author supplied keywords
Cite
CITATION STYLE
Xu, C., Liu, Z., & Tan, J. K. (2022). Fruits and Vegetables Detection using the Improved YOLOv3. In Proceedings of International Conference on Artificial Life and Robotics (pp. 456–460). ALife Robotics Corporation Ltd. https://doi.org/10.5954/icarob.2022.os21-6
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.