| 基于YOLO v3的生猪个体识别方法 |
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| 引用本文： | 金耀,何秀文,万世主,刘仁鑫. 基于YOLO v3的生猪个体识别方法[J]. 中国农机化学报, 2021, 0(2): 178-183 |
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| 作者姓名： | 金耀 何秀文 万世主 刘仁鑫 |
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| 作者单位： | 江西农业大学工学院 |
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| 基金项目： | 江西省畜牧设施技术开发工程研究中心专项基金(赣发改高技(2019)277号)。 |
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| 摘 要： | 为实现高效的猪只个体识别,提出一种基于机器视觉的生猪个体识别方法.通过采集母猪和仔猪个体图像,对图像进行扩充和筛选,制作训练集和测试集.试验采用基于YOLO v3的识别模型,并与Faster RCNN和SSD模型识别结果进行比较,结果表明:对仔猪的识别平均精度均值达89.65％,准确率达95.99％,召回率达84.09...
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| 关 键 词： | 猪只识别 机器视觉 深度学习 YOLO v3 图像采集 养殖信息化 |
Individual pig identification method based on YOLO v3 |
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| Jin Yao,He Xiuwen,Wan Shizhu,Liu Renxin. Individual pig identification method based on YOLO v3[J]. Chinese Agricultural Mechanization, 2021, 0(2): 178-183 |
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| Authors: | Jin Yao He Xiuwen Wan Shizhu Liu Renxin |
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| Affiliation: | (School of Engineering,Jiangxi Agricultural University,Nanchang,330045,China) |
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| Abstract: | In order to realize efficient pig individual recognition,a pig recognition method based on machine vision was proposed.Training sets and test sets were made by collecting individual images of sows and piglets,and the images were expanded and screened.The identification model based on YOLO v3 was adopted and compared with the identification results of the Faster RCNN and SSD models.The results showed that the mean average precision of pig lets identification was 89.65%,the accuracy rate was 95.99%,and the recall rate was 84.09%.The mean average precision of sows identification was 95.16%,the accuracy rate was 96.00%,and the recall rate was 96.00%.The recognition rate of the model is 7 times higher than that of the Faster RCNN,and the average accuracy of the model is 9 percentage points higher than that of the SSD.The model reaches a high level in both the recognition rate and the recognition accuracy.This study can provide theoretical basis for intelligent identification,data monitoring and breeding informatization of pigs. |
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| Keywords: | pig identification machine vision deep learning YOLO v3 image acquisition aquaculture informatization |
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