ITM Web Conf.
Volume 54, 20232nd International Conference on Advances in Computing, Communication and Security (I3CS-2023)
|Number of page(s)||8|
|Published online||04 July 2023|
Efficient Pomegranate Segmentation with UNet: A Comparative Analysis of Backbone Architectures and Knowledge Distillation
1 Department of Electronics and Telecommunication Engineering, COEP, Technological University, India - 411005
2 Department of Farm Implements and Machinery, Mahatma Phule Krishi Vidyapeeth, Rahuri - 413722
This work examines the segmentation of on-field images of pomegranate fruit using UNet model with different backbones. Precise and effective segmentation of pomegranate fruits on the field is essential for automating yield estimation, disease detection, and quality evaluation in the agricultural industry. The models have been trained and validated using actual images captured in a pomegranate field. The study assesses the performance of many backbones, including ResNet50, Inception ResNetV2, MobileNetv2, DenseNet121, EfficientNet, VGG16, and VGG19. The VGG19 backbone achieved the highest F1 score, 90.35%, according to the data. In addition, we employed feature-based knowledge distillation to move the knowledge from the VGG19 backbone to the lighter MobileNetv2 backbone (45x smaller than VGG19 in number of parameters), which increased the F1 score of MobileNetv2 from 86.97% to 89.91%. Our findings show that the effectiveness of the UNet model for pomegranate fruit segmentation is greatly impacted by the selection of the backbone architecture, and that knowledge distillation can improve the accuracy of UNet models with lighter backbones without significantly increasing their computational complexity.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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