Abstract
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 p-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 (p-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.