Song Shihao, Guo Qiqi, Duan Xiaosa, Shi Xiaojing, Liu Zhenyu
School of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
College of Agricultural Engineering, Shanxi Agricultural University, Jinzhong 030801, China.
Foods. 2024 Dec 10;13(24):3986. doi: 10.3390/foods13243986.
With the increasing importance of meat quality inspection, traditional manual evaluation methods face challenges in terms of efficiency and accuracy. To improve the precision and efficiency of pork quality assessment, an automated detection method based on computer vision technology is proposed for evaluating different parts and freshness of pork. First, high-resolution cameras were used to capture image data of Jinfen white pigs, covering three pork cuts-hind leg, loin, and belly-across three different collection times. These three parts were categorized into nine datasets, and the sample set was expanded through digital image processing techniques. Next, five convolutional neural network models-VGGNet, ResNet, DenseNet, MobileNet, and EfficientNet-were selected for feature recognition experiments. The experimental results showed that the MobileNetV3_Small model achieved an accuracy of 98.59%, outperforming other classical network architectures while being more lightweight. Further statistical analysis revealed that the -values for ResNet101, EfficientNetB0, and EfficientNetB1 were all greater than 0.05, indicating that the performance differences between these models and MobileNetV3_Small were not statistically significant. In contrast, other models showed significant performance differences (-value < 0.05). Finally, based on the PYQT5 framework, the MobileNetV3_Small model was deployed on a local client, realizing an efficient and accurate end-to-end automatic recognition system. These findings can be used to effectively enhance the efficiency and reliability of pork quality detection, providing a solid foundation for the development of pork safety monitoring systems.
随着肉质检测的重要性日益增加，传统的人工评估方法在效率和准确性方面面临挑战。为了提高猪肉质量评估的精度和效率，提出了一种基于计算机视觉技术的自动检测方法，用于评估猪肉的不同部位和新鲜度。首先，使用高分辨率相机采集金粉白猪的图像数据，涵盖后腿、腰肉和腹部这三个猪肉切块，采集时间分为三个不同时段。这三个部位被分类为九个数据集，并通过数字图像处理技术扩大样本集。接下来，选择了五个卷积神经网络模型——VGGNet、ResNet、DenseNet、MobileNet和EfficientNet——进行特征识别实验。实验结果表明，MobileNetV3_Small模型的准确率达到了98.59%，优于其他经典网络架构，同时更加轻量级。进一步的统计分析表明，ResNet101、EfficientNetB0和EfficientNetB1的p值均大于0.05，表明这些模型与MobileNetV3_Small之间的性能差异在统计学上不显著。相比之下，其他模型表现出显著的性能差异（p值<0.05）。最后，基于PYQT5框架，将MobileNetV3_Small模型部署在本地客户端，实现了高效准确的端到端自动识别系统。这些研究结果可用于有效提高猪肉质量检测的效率和可靠性，为猪肉安全监测系统的发展提供坚实基础。
