基于机器视觉的肉鸡胴体淤血检测技术

肉鸡胴体淤血是一种品质异常现象，给屠宰企业带来较大的经济损失。建立胴体淤血的快速、准确识别技术是产业当前的迫切需求。该研究利用自行设计搭建的肉鸡胴体图像采集装置，研发了一种快速识别胴体淤血的技术方法。采用三方位视觉采集（搭载三光源）系统，实现视场对肉鸡胴体的全覆盖。采用基于全局RGB阈值分割提取出图像的14个特征参数，采用主成分分析降维后得到7个主成分，结合遗传算法训练支持向量机模型。然后基于滑动窗口分割胴体子图像，人工将子图像分为四类并提取出颜色矩信息，结合遗传算法训练支持向量机模型并采用相似性度量对模型分类结果进行修正。发现正视图和侧视图中基于7个主成分的支持向量机模型中，分类准确率分别为86.0%和89.8%，预测时间为0.006 s，RGB阈值分割淤血的效果不理想；基于局部颜色矩支持向量机模型中，分类准确率分别为98.3%和97.9%，预测时间为0.001 s。在测试样本上，结合欧氏距离进行相似性度量对模型分类结果修正后，淤血的识别召回率得到提升，误报率和漏报率降低。该研究提出的基于胴体子图像局部颜色矩信息训练支持向量机模型结合相似性度量方法，可以弥补全局RGB阈值分割淤血的不足，有效识别胴体淤血，为工厂进行胴体淤血的实时检测提供参考。

Broiler carcass congestion detection technology using machine vision

Abstract: Poultry carcass defects and condemnation (such as bruises) have posed a great threat to the broiler industry in recent years. Among abnormal quality, the broiler carcass congestion has also brought serious economic losses to slaughtering enterprises. It is an urgent need to establish a rapid and accurate identification of carcass congestion in the industry. In this study, an image acquisition device was developed for the rapid detection of broiler carcass congestion using machine vision. A three-directional visual acquisition system (equipped with three light sources) was adopted to realize the full coverage of the broiler carcass in the field of view. The image was also captured and then preprocessed using the grayscale, Gaussian filter denoising, binarization, and morphological processing. The maximum circumscribed rectangle of the carcass was obtained, and then the image was divided into the front and side view. Firstly, the global RGB color threshold was determined to segment the congestion in the carcass image. 14 characteristic parameters of the image were also extracted, including the R, G, B, H, I, S, a*, b*, Ar1 (the total area of the first type of congestion), Ar2 (the total area of the second type of congestion), Pc1 (the percentage of the first type of congestion), Pc2 (the percentage of the second type of congestion), M1 (the largest area in the first type of congestion), and M2 (the largest area in the second type of congestion). A Pearson correlation analysis was performed on the characteristic parameters. A principal component analysis was implemented to obtain the seven principal components after dimension reduction. The classification model was trained using a Support Vector Machine (SVM) combined with the Genetic Algorithm (GA). Secondly, the maximum circumscribed rectangular images of carcasses were traversed using the sliding window. The sub-images of (50 × 50) pixels were then divided into the images. The calibration coefficient was integrated to determine the real area of a sub-image (6 cm2) for each image. The sub-images were then divided into four categories, namely congestion, normal skin, carcass-background junction, and background. Four types of large sub-images with outstanding characteristics were manually selected to extract the color moment. The SVM-GA model was also achieved in this case. Finally, the similarity measure was used to revise the classification of the model. The results show that the classification accuracies of the SVM model using seven principal components were 86.0% and 89.8%, respectively, in the front and side view. The prediction time was 0.006 s. Nevertheless, there was no ideal effect of RGB threshold segmentation. By contrast, the classification accuracies of the SVM model using color moment were 98.3% and 97.9%, respectively, where the prediction time was 0.001 s. Furthermore, the recognition recall rate of congestion in the test sample was improved significantly after the revision of the model combining the Euclidean distance with the similarity measure. More importantly, there was a great decrease in the false positive rate and the miss rate. Consequently, the trained SVM model with the similarity measurement can be expected to effectively identify the carcass congestion using the local color moment of carcass sub-images, compared with the global RGB threshold segmentation at present. The finding can provide a strong reference to conduct the real-time detection of carcass congestion in poultry production.