Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3

Haiping Si, Yunpeng Wang, Wenrui Zhao, Ming Wang, Jiazhen Song, Li Wan, Zhengdao Song, Yujie Li, Bacao Fernando and Changxia Sun ()
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Haiping Si: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Yunpeng Wang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Wenrui Zhao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Ming Wang: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Jiazhen Song: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Li Wan: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Zhengdao Song: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Yujie Li: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Bacao Fernando: NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, 1099-085 Lisbon, Portugal
Changxia Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2023, vol. 13, issue 4, 1-26

Abstract: Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affect their economic performance. Therefore, the timely detection of apples with surface defects can effectively reduce economic losses. In this paper, we propose an apple surface defect detection method based on weight contrast transfer and the MobileNetV3 model. By means of an acquisition device, a thermal, infrared, and visible apple surface defect dataset is constructed. In addition, a model training strategy for weight contrast transfer is proposed in this paper. The MobileNetV3 model with weight comparison transfer (Weight Compare-MobileNetV3, WC-MobileNetV3) showed a 16% improvement in accuracy, 14.68% improvement in precision, 14.4% improvement in recall, and 15.39% improvement in F1-score. WC-MobileNetV3 compared to MobileNetV3 with fine-tuning improved accuracy by 2.4%, precision by 2.67%, recall by 2.42% and F1-score by 2.56% compared to the classical neural networks AlexNet, ResNet50, DenseNet169, and EfficientNetV2. The experimental results show that the WC-MobileNetV3 model adequately balances accuracy and detection time and achieves better performance. In summary, the proposed method achieves high accuracy for apple surface defect detection and can meet the demand of online apple grading.

Keywords: defect detection; image fusion; deep learning; transfer learning; weight comparison (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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