基于红外与可见光图像融合的苹果表面缺陷检测方法

目的：解决目前中国苹果分级分类大部分情况下仍需要进行人工筛选的问题。方法：采用基于多尺度变换的红外与可见光图像融合算法对所采集到的苹果的可见光图像和红外图像进行融合，得到缺陷特征更加直观的融合图像，对该图像进行图像的预处理操作得到二值化图像数据集，再采用卷积神经网络的AlexNet模型对之前的苹果表面缺陷数据集进行训练、验证和检测。结果：该检测方法在所制作的苹果表面缺陷数据集上对完好果、缺陷果、花萼/果梗、花萼/果梗加缺陷识别的平均准确度为99.0%，其中对花萼/果梗的识别准确率可达95.8%，对完好果、缺陷果和花萼/果梗加缺陷的识别准确率高达100%。结论：该方法对苹果表面缺陷的检测精度比较高，可以满足对苹果的在线分级的需求。

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

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.