Detecting strawberries using different Convolutional Neural Networks

Detecting strawberries using different Convolutional Neural Networks

Bechtold, Jeroen (TU Delft Electrical Engineering, Mathematics and Computer Science)

Abeel, T.E.P.M.F. (mentor)
Wen, J. (mentor)
Nadeem, A. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-06-21

This paper tries to combat the food waste of strawberries during the harvesting steps.
An automatic pipeline must be established to combat this food waste.
One of the steps needed in this pipeline is detecting strawberries in images.
Therefore, this paper aims to find out which Convolutional Neural Network (CNN) can be best used to detect strawberries.
Faster r-cnn, Mask r-cnn and RetinaNet are compared against each other using different setting.
Mask r-cnn achieved the highest average bounding box and segmentation mAP with 51.63 and 73.20 respectively.

convolutional neural network
deep learning
segmentation
Strawberry

https://doi.org/10.4233/uuid:5fbcd994-c76f-49e9-9db3-0a95b6296ac2

Student theses

bachelor thesis

© 2022 Jeroen Bechtold