Apple surface defect detection method based on fusion of infrared and visible images

Authors

WANG Yun-peng, College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, China
SI Hai-ping, College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, China
SONG Jia-zhen, College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, China
WAN Li, College of Information and Management Science, Henan Agricultural University, Zhengzhou, Henan 450046, China

Abstract

Objective: To solve the current situation of manual screening in most cases of Chinese apple classification. Methods: The infrared and visible image fusion algorithm based on multi-scale transformation was used to fuse the collected visible image and infrared image of the apple to obtain a more intuitive fusion image with defect characteristics, and performed image preprocessing operations on the image to obtain a binary value. The image data set was transformed, and then the AlexNet model of the convolutional neural network was used to train, verify and detect the previous Apple surface defect data set. Results: The detection method has an average accuracy of 99.0% for intact fruit, defective fruit, calyx/stalk, calyx/stalk plus defect on the produced apple surface defect data set, and the average accuracy was 99.0%. The recognition accuracy rate could reach 95.8%, and the recognition accuracy rate of intact fruit, defective fruit and calyx/fruit stem plus defect was as high as 100%. Conclusion: This method has a relatively high detection accuracy for apple surface defects, which can meet the demand for online classification of apples.

Publication Date

12-28-2021

First Page

127

Last Page

131

DOI

10.13652/j.issn.1003-5788.2021.12.021

Recommended Citation

Yun-peng, WANG; Hai-ping, SI; Jia-zhen, SONG; and Li, WAN (2021) "Apple surface defect detection method based on fusion of infrared and visible images," Food and Machinery: Vol. 37: Iss. 12, Article 21.
DOI: 10.13652/j.issn.1003-5788.2021.12.021
Available at: https://www.ifoodmm.cn/journal/vol37/iss12/21

References

[1] 邱光应. 基于机器视觉的苹果果梗/花萼与缺陷识别[D]. 重庆: 西南大学, 2017: 4-9.
[2] 彭步迅, 张晓, 任显丞, 等. 无损检测技术在水果轻微损伤检测中的应用研究[J]. 现代食品, 2020（23）: 142-144.
[3] 邱光应, 彭桂兰, 陶丹, 等. 基于决策树支持向量机的苹果表面缺陷识别[J]. 食品与机械, 2017, 33（9）: 131-135.
[4] 孟庆龙, 张艳, 尚静. 基于高光谱成像技术的苹果表面缺陷无损检测[J]. 食品研究与开发, 2019, 40（5）: 168-172.
[5] 高辉, 马国峰, 刘伟杰. 基于机器视觉的苹果缺陷快速检测方法研究[J]. 食品与机械, 2020, 36（10）: 125-129, 148.
[6] 陈乾辉, 吴德刚. 基于可见光—红外光图像融合的苹果缺陷检测算法[J]. 食品与机械, 2018, 34（9）: 135-138.
[7] LI H, WU X J, KITTLER J. Infrared and visible image fusion using a deep learning framework[C]// Pattern Recognition （ICPR）, 2018 24rd International Conference on. [S.l.]: IEEE, 2018: 2 705-2 710.
[8] LI Shu-tao, KANG Xu-dong, HU Jian-wen. Image fusion with guided filtering[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2013, 22（7）: 2 864-2 875.
[9] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[J]. arXiv Preprint arXiv, 2014（9）: 1-14.
[10] 李星恕, 张博, 靳莉珍, 等. 加热温度对苹果组织中细胞阻抗特性及水分分布的影响[J]. 农业工程学报, 2015, 31（14）: 284-290.
[11] DABOV K, FOI A, KATKOVNIK V, et al. Image denoising by sparse 3-D transform-domain collaborative filtering[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 2007, 16（8）: 2 080-2 095.
[12] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. International Conference on Neural Information Processing Systems, 2012, 60（2）: 1 097-1 105.