Tang Wenquan, Zhang Hao, Chen Haoran, Fan Wei, Wang Qiaohua
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China.
Ministry of Agriculture Key Laboratory of Agricultural Equipment in the Middle and Lower Reaches of the Yangtze River, Wuhan 430070, China.
Foods. 2024 Mar 19;13(6):925. doi: 10.3390/foods13060925.
As a traditional delicacy in China, preserved eggs inevitably experience instances of substandard quality during the production process. Chinese preserved egg production facilities can only rely on experienced workers to select the preserved eggs. However, the manual selection of preserved eggs presents challenges such as a low efficiency, subjective judgments, high costs, and hindered industrial production processes. In response to these challenges, this study procured the transmitted imagery of preserved eggs and refined the ConvNeXt network across four pivotal dimensions: the dimensionality reduction of model feature maps, the integration of multi-scale feature fusion (MSFF), the incorporation of a global attention mechanism (GAM) module, and the amalgamation of the cross-entropy loss function with focal loss. The resultant refined model, ConvNeXt_PEgg, attained proficiency in classifying and grading preserved eggs. Notably, the improved model achieved a classification accuracy of 92.6% across the five categories of preserved eggs, with a grading accuracy of 95.9% spanning three levels. Moreover, in contrast to its predecessor, the refined model witnessed a 24.5% reduction in the parameter volume, alongside a 3.2 percentage point augmentation in the classification accuracy and a 2.8 percentage point boost in the grading accuracy. Through meticulous comparative analysis, each enhancement exhibited varying degrees of performance elevation. Evidently, the refined model outshone a plethora of classical models, underscoring its efficacy in discerning the internal quality of preserved eggs. With its potential for real-world implementation, this technology portends to heighten the economic viability of manufacturing facilities.
作为中国的传统美食，皮蛋在生产过程中不可避免地会出现质量不达标的情况。中国皮蛋生产企业只能依靠经验丰富的工人来挑选皮蛋。然而，人工挑选皮蛋存在效率低、主观判断、成本高以及阻碍工业生产流程等挑战。针对这些挑战，本研究获取了皮蛋的透射图像，并在四个关键维度上对ConvNeXt网络进行了优化：模型特征图降维、多尺度特征融合（MSFF）集成、全局注意力机制（GAM）模块的引入以及交叉熵损失函数与焦点损失的融合。由此得到的优化模型ConvNeXt_PEgg在皮蛋分类和分级方面表现出色。值得注意的是，改进后的模型在五类皮蛋上的分类准确率达到了92.6%，在三个等级上的分级准确率达到了95.9%。此外，与之前的模型相比，优化后的模型参数量减少了24.5%，分类准确率提高了3.2个百分点，分级准确率提高了2.8个百分点。通过细致的对比分析，各项改进均呈现出不同程度的性能提升。显然，优化后的模型优于众多经典模型，凸显了其在辨别皮蛋内部质量方面的有效性。鉴于其在实际应用中的潜力，这项技术有望提高生产企业的经济可行性。
