CN112184322A - 一种基于图片识别的活体禽畜抵押贷款贷前估值方法 - Google Patents
一种基于图片识别的活体禽畜抵押贷款贷前估值方法 Download PDFInfo
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Abstract
本发明公开了一种基于图片识别的活体禽畜抵押贷款贷前估值方法,包括如下步骤:建立优质畜禽图片数据库;获取图片数据库内家禽类、畜牧类图片数据,建立图片数据集;将数据集内图片进行标注处理;将生成带标注的图片数据集划分为训练集和测试集;训练集作为训练模型输入,导出离线训练yolov4动物数量统计模型;测试集作为学习模型输入,调整参数,得到最优的yolov4动物数量统计模型;采集待估值养殖场内畜禽照片;将待估值养殖场内畜禽照片输入最优的yolov4动物数量统计模型中;畜禽价值评估。本发明的有益效果:解决了因缺乏统一质量评估标准规范,对禽畜数量及价值估算不准确的问题。
Description
技术领域
本发明涉及活体禽畜抵押贷款估值技术领域,具体的,涉及一种基于图片识别的活体禽畜抵押贷款贷前估值方法。
背景技术
目前现有贷前调查中对抵质押物进行调查的手段,一般有几种,一是直接的现场调研,通过和贷款人现场会谈、实地考察,查看抵质押活体畜禽的真实情况;二是委托调查,通过中介机构或银行自身网络开展调查。针对活体畜禽抵押贷款,金融机构会对借款人的抵押物,也就是活体畜禽进行价值评估,这个一般是贷前调查人员现场到养殖企业或者农户家进行核查。贷前调查人员到现场后,根据自己的经验,对活体畜禽的数量、质量、总价值进行评估。
但是活体禽畜抵押贷款属于新兴业务,对贷前调查人员要求较高,且由于没有统一的质量评估标准规范,对于活体禽畜的数量及总价值的估算可能存在误差。
发明内容
本发明的目的在于,针对上述问题,提出一种基于图片识别的活体禽畜抵押贷款贷前估值方法。
一种基于图片识别的活体禽畜抵押贷款贷前估值方法,包括如下步骤:
S1:建立优质畜禽图片数据库;
S2:获取图片数据库内家禽类、畜牧类图片数据,建立图片数据集;
S3:将数据集内图片进行标注处理;
S4:将生成带标注的图片数据集划分为训练集和测试集;
S5:训练集作为训练模型输入,导出离线训练yolov4动物数量统计模型;
S6:测试集作为学习模型输入,调整参数,得到最优的yolov4动物数量统计模型;
S7:采集待估值养殖场内畜禽照片;
S8:将待估值养殖场内畜禽照片输入最优的yolov4动物数量统计模型中;
S9:畜禽价值评估。
优选的,所述步骤S3中需对数据集中的每一张图片进行标注,在图片中框选目标区域,并标注框选类别。
优选的,所述步骤S4中图片数据集按照8:2的比例随机划分训练集和测试集。
优选的,所述步骤S5中离线训练yolov4动物数量统计模型利用卷积神经网络(CNN),同时提取适用于大多数模型、任务和数据集的通用特征。
优选的,所述通用特征的处理包括:使用加权残差连接(WRC)、阶段部分连接(CSP)、交叉小批量标准化(CmB N)、自对抗训练(SAT)、Mish激活、马赛克数据增强、CmBN、DropBlock正则化和CIoU丢失。
本发明的有益效果:通过建立训练集和测试集,构建最优yolov4动物数量统计模型,对待估值养殖场内禽畜进行优质禽畜数量统计并进行估值;解决了因缺乏统一质量评估标准规范,对禽畜数量及价值估算不准确的问题。
附图说明
图1为本发明的具体实现流程。
具体实施方式
下面结合附图对本发明做进一步的描述。
如图1所示,一种基于图片识别的活体禽畜抵押贷款贷前估值方法,包括如下步骤:
S1:建立优质畜禽图片数据库;
S2:获取图片数据库内家禽类、畜牧类图片数据,建立图片数据集;
S3:将数据集内图片进行标注处理;
S4:将生成带标注的图片数据集划分为训练集和测试集;
S5:训练集作为训练模型输入,导出离线训练yolov4动物数量统计模型;
S6:测试集作为学习模型输入,调整参数,得到最优的yolov4动物数量统计模型;
S7:采集待估值养殖场内畜禽照片;
S8:将待估值养殖场内畜禽照片输入最优的yolov4动物数量统计模型中;
S9:畜禽价值评估。
需要理解的是,所述步骤S3中需对数据集中的每一张图片进行标注,在图片中框选目标区域,并标注框选类别。
需要理解的是,所述步骤S4中图片数据集按照8:2的比例随机划分训练集和测试集。
需要理解的是,所述步骤S5中离线训练yolov4动物数量统计模型利用卷积神经网络(CNN),同时提取适用于大多数模型、任务和数据集的通用特征。
需要理解的是,所述通用特征的处理包括:使用加权残差连接(WRC)、阶段部分连接(CSP)、交叉小批量标准化(CmB N)、自对抗训练(SAT)、Mish激活、马赛克数据增强、CmBN、DropBlock正则化和CIoU丢失。
Claims (5)
1.一种基于图片识别的活体禽畜抵押贷款贷前估值方法,其特征在于,包括如下步骤:
S1:建立优质畜禽图片数据库;
S2:获取图片数据库内家禽类、畜牧类图片数据,建立图片数据集;
S3:将数据集内图片进行标注处理;
S4:将生成带标注的图片数据集划分为训练集和测试集;
S5:训练集作为训练模型输入,导出离线训练yolov4动物数量统计模型;
S6:测试集作为学习模型输入,调整参数,得到最优的yolov4动物数量统计模型;
S7:采集待估值养殖场内畜禽照片;
S8:将待估值养殖场内畜禽照片输入最优的yolov4动物数量统计模型中;
S9:畜禽价值评估。
2.如权利要求1所述一种基于图片识别的活体禽畜抵押贷款贷前估值方法,其特征在于,所述步骤S3中需对数据集中的每一张图片进行标注,在图片中框选目标区域,并标注框选类别。
3.如权利要求1所述一种基于图片识别的活体禽畜抵押贷款贷前估值方法,其特征在于,所述步骤S4中图片数据集按照8:2的比例随机划分训练集和测试集。
4.如权利要求1所述一种基于图片识别的活体禽畜抵押贷款贷前估值方法,其特征在于,所述步骤S5中离线训练yolov4动物数量统计模型利用卷积神经网络(CNN),同时提取适用于大多数模型、任务和数据集的通用特征。
5. 如权利要求4所述一种基于图片识别的活体禽畜抵押贷款贷前估值方法,其特征在于,所述通用特征的处理包括:使用加权残差连接(WRC)、阶段部分连接(CSP)、交叉小批量标准化(CmB N)、自对抗训练(SAT)、Mish激活、马赛克数据增强、CmBN、DropBlock正则化和CIoU丢失。
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CN114781775A (zh) * | 2022-01-10 | 2022-07-22 | 上海皓卡网络技术有限公司 | 一种多环节远程图像及信号管控的智慧管理系统 |
CN115311831A (zh) * | 2022-08-01 | 2022-11-08 | 太湖流域管理局苏州管理局 | 一种电气柜监测系统及方法 |
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CN111709372A (zh) * | 2020-06-18 | 2020-09-25 | 深圳市赛为智能股份有限公司 | 驱鸟方法、装置、计算机设备及存储介质 |
CN111709374A (zh) * | 2020-06-18 | 2020-09-25 | 深圳市赛为智能股份有限公司 | 鸟情检测方法、装置、计算机设备及存储介质 |
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US20050257748A1 (en) * | 2002-08-02 | 2005-11-24 | Kriesel Marshall S | Apparatus and methods for the volumetric and dimensional measurement of livestock |
CN108805137A (zh) * | 2018-04-17 | 2018-11-13 | 平安科技(深圳)有限公司 | 牲畜特征向量的提取方法、装置、计算机设备和存储介质 |
CN111709372A (zh) * | 2020-06-18 | 2020-09-25 | 深圳市赛为智能股份有限公司 | 驱鸟方法、装置、计算机设备及存储介质 |
CN111709374A (zh) * | 2020-06-18 | 2020-09-25 | 深圳市赛为智能股份有限公司 | 鸟情检测方法、装置、计算机设备及存储介质 |
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CN114781775A (zh) * | 2022-01-10 | 2022-07-22 | 上海皓卡网络技术有限公司 | 一种多环节远程图像及信号管控的智慧管理系统 |
CN115311831A (zh) * | 2022-08-01 | 2022-11-08 | 太湖流域管理局苏州管理局 | 一种电气柜监测系统及方法 |
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