基于机器视觉的鸡胴体断翅快速检测技术  

Rapid detection technology for broken-winged broiler carcass based on machine vision

作　　者：吴江春 王虎虎[1] 徐幸莲[1] Wu Jiangchun;Wang Huhu;Xu Xinglian(Key Laboratory of Meat Processing and Quality Control,Ministry of Education,Nanjing Agricultural University,Nanjing 210095,China)

机构地区：[1]南京农业大学肉品加工与质量控制教育部重点实验室,南京210095

出　　处：《农业工程学报》2022年第22期253-261,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家现代农业产业技术体系项目(CARS-41)。

摘　　要：为实现肉鸡屠宰过程中断翅鸡胴体的快速检测,提高生产效率,该研究利用机器视觉系统采集了肉鸡屠宰线上的1 053张肉鸡胴体图,构建了一种快速识别断翅缺陷的方法。通过机器视觉装置采集鸡胴体正视图,经图像预处理后分别提取鸡胴体左右两端到质心的距离及其差值(d1、d2、dc)、两翅最低点高度及其差值(h1、h2、hc)、两翅面积及其比值(S1、S2、Sr)、矩形度(R)和宽长比(rate)共11个特征值,并通过主成分分析降维至8个主成分。建立线性判别模型、二次判别模型、随机森林、支持向量机、BP神经网络和VGG16模型,比较模型的F1分数和总准确率,在所有模型组合中,以VGG16模型的F1分数和总准确率最高,分别为94.35%和93.28%,平均预测速度为10.34张/s。利用VGG16建立的模型有较好的分类效果,可为鸡胴体断翅的快速识别与分类提供技术参考。Broken-winged chicken carcasses can be one of the most common defects in broiler slaughter plants. Manual detection cannot fully meet the large-scale production, due to the high labor intensity with the low efficiency and accuracy.Therefore, it is a high demand to rapidly and accurately detect broken wings on chicken carcasses. This study aims to realize the rapid inspection of broken-winged chicken carcasses in the progress of broiler slaughter, in order to improve the production efficiency for the cost-saving slaughter line. 1053 broiler carcass images were collected from a broiler slaughter line using a computer vision system. Rapid identification was then constructed for the broken wing defects. Specifically, the front view of the chicken carcass was obtained in the machine vision system. The preprocessing was then deployed to obtain the chicken carcass images without the background, including the weighted average(graying), two-dimensional median filtering(denoising), and iterative(threshold segmentation). The code was also written in the MATLAB platform. After that, a total of 11 characteristic values were calculated, covering the exact distance starting from the left and right ends of the chicken carcass image to the centroid and the difference(d1, d2, and dc), the heights of the lowest point in the two wings and their difference(h1, h2, and hc), the areas of the two wings and ratio of them(S1, S2, and Sr), squareness(R), and width-length ratio(rate). As such, the eight principal components were achieved in the principal component analysis after the reduction of several dimensions. Separately, the principal components and characteristic values were imported into the specific model of linear discriminant analysis(LDA), quadratic discriminant analysis(QDA), random forest(RF), support vector machine(SVM), and BP neural network. Among them, the input parameter of the VGG16 model was from the RGB maps of the chicken carcass with the removed background. Finally, a comparison was made for the F1-scores and total a

关 键 词：机器视觉 机器学习 鸡胴体 断翅检测 

分 类 号：TS251.7[轻工技术与工程—农产品加工及贮藏工程]