Authors

Abstract

The skin of Yu-Lu-Xiang pears is brittle and easily damaged during picking and sorting. In order to reduce the secondary damage caused by mechanical automatic sorting of Yu-Lu-Xiang pear after harvest, optimize the sorting process and improve the sorting accuracy. Based on the MobileV2Net model, a lightweight convolutional neural network model EC-MobileV2Net-Fast, which integrated transfer learning and attention mechanism, was proposed to identify skin damage defects of Yu-Lu-Xiang pears. According to the defects of Yu-Lu-Xiang pears with different damage degrees, a dataset containing four characteristics was created. The model accuracy rate, single defect identification accuracy rate, recall, specificity, parameter and so on were taken as evaluation indexes, and the interpretation ability of the model decision was analyzed by Grad-CAM thermal map. Preliminary evaluation results showed that the model produced the highest level of accuracy, underscoring the potential of deep learning algorithms to significantly enhance defect recognition and classification. It can improve sorting efficiency, reduce labor costs and strictly control after-sales quality.

Abstract in Chinese

玉露香梨的果皮极其脆弱，在采摘和分拣过程中容易损坏。为了减少玉露香梨收获后机械自动分拣造成的二次损害，优化分拣工艺，提高分拣精度。本文以MobileV2Net模型为基础，提出了一种融合迁移学习和注意力机制的轻量化卷积神经网络模型EC-MobileV2Net-Fast用以识别玉露香梨表皮损伤缺陷。根据玉露香梨不同损伤程度所表现的缺陷创建了含有4种特征的数据集。以模型准确率、单一缺陷识别精确率、灵敏度、特异度、参数量等作为评价指标，采用Grad-CAM热力图分析模型决策的解释能力。初步评估结果表明, 模型产生了最高水平的准确性，强调了深度学习算法在显著增强玉露香梨缺陷识别分类方面的潜力。可以提高分选效率、降低劳动力成本和严格把控售后质量。