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Potato Surface Defect Detection Based on Deep Transfer Learning

delete2021-09-10
delete26
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OA
AI
C
Chenglong Wang
Z
Zhifeng Xiao *
DOI:10.3390/agriculture11090863delete
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摘要

摘要

En 中文
Food defect detection is crucial for the automation of food production and processing. Potato surface defect detection remains challenging due to the irregular shape of potato individuals and various types of defects. This paper employs deep convolutional neural network (DCNN) models for potato surface defect detection. In particular, we applied transfer learning by fine-tuning a base model through three DCNN models-SSD Inception V2, RFCN ResNet101, and Faster RCNN ResNet101-on a self-developed dataset, and achieved an accuracy of 92.5%, 95.6%, and 98.7%, respectively. RFCN ResNet101 presented the best overall performance in detection speed and accuracy. It was selected as the final model for out-of-sample testing, further demonstrating the model's ability to generalize.
Keyword:
potato surface defect detection
deep convolutional neural networks
SSD
faster RCNN
object detection

期刊

Agriculture 封面图
Agriculture
IF:
3.6
论文数:
1.3W
被引数:
2.8W

机构

H
Huizhou University
学者数:
895
论文数: 848
被引数: 1.4K
P
pennsylvania commonwealth system of higher education (pcshe)
学者数:
12.9W
论文数: 11.7W
被引数: 177
引用论文

引用论文

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IF0
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Robustness and generalization
err2011-11-15
err246
errOAAI
errXu, Huan; Mannor, Shie
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