Potato Surface Defect Detection Based on Deep Transfer Learning

Agriculture

Chenglong Wang¹  Zhifeng Xiao² 

[1] School of Electronic Information and Electrical Engineering, Huizhou University, Huizhou 516007, China;School of Engineering, Penn State Erie, The Behrend College, Erie, PA 16563, USA;

关键词: potato surface defect detection;    deep convolutional neural networks;    SSD;    faster RCNN;    object detection;   

DOI  :  10.3390/agriculture11090863

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown