生猪虹膜快速定位算法研究

doi:10.3969/j.issn.1004-1524.20231274

浙江农业学报 ›› 2024, Vol. 36 ›› Issue (11): 2617-2626.DOI: 10.3969/j.issn.1004-1524.20231274

生猪虹膜快速定位算法研究

刘伟东^a(), 周素茵^a^,^*(), 徐爱俊^a, 叶俊华^b

a.数学与计算机科学学院, 浙江农林大学, 浙江杭州 311300
b.环境与资源学院,浙江农林大学, 浙江杭州 311300

收稿日期:2023-11-13 出版日期:2024-11-25 发布日期:2024-11-27
作者简介:刘伟东(1999—),男,江苏扬州人,硕士研究生,研究方向为农业信息化。E-mail:liuweidong@ztt.cn
通讯作者: *周素茵,E-mail:zsy197733@163.com
基金资助:
浙江省“领雁”研发攻关计划(2022C02050);浙江省“三农九方”科技协作计划(2022SNJF057)

Research on fast localization algorithm of pig iris

LIU Weidong^a(), ZHOU Suyin^a^,^*(), XU Aijun^a, YE Junhua^b

a. College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China
b. College of Environment and Resources, Zhejiang A&F University, Hangzhou 311300, China

Received:2023-11-13 Online:2024-11-25 Published:2024-11-27

摘要/Abstract

摘要：

虹膜定位是实现虹膜识别的前提与关键,当前主流虹膜采集设备普遍价格昂贵,且现有虹膜定位算法应用于猪眼虹膜定位时存在精度低、耗时长等问题。本文提出一种基于低成本采集设备的生猪虹膜快速高精度定位算法。首先对猪眼虹膜图像去噪和二值化,再对Canny算法进行改进,用于提取猪眼虹膜图像边缘信息,然后分别使用改进的Hough圆变换和迭代加权最小二乘法定位虹膜内、外边缘,进而完成生猪虹膜定位。对3 100张猪眼虹膜图像进行试验,结果表明,虹膜的正确检测率为96.19%,平均定位时间为342.50 ms。与DAUGMAN、WILDES和单位扇环灰度算法相比,正确检测率分别提升了10.49、8.67、3.61百分点,平均定位时间分别减少了70.90、81.50、98.00 ms。本文方法能在保证生猪虹膜正确检测率的基础上缩短定位时间,有效改善传统虹膜定位算法应用于猪眼虹膜时效果较差的问题,能为后续基于虹膜的生猪身份识别奠定基础。

关键词: 生猪, 虹膜定位, Canny算法, Hough圆变换, 正确检测率

Abstract:

Iris localization is the prerequisite and key link to realize iris recognition, the current mainstream iris capture devices are generally expensive, and the existing iris localization algorithm have problems such as low accuracy and long time consumption when applied to pig eye iris localization, so this paper proposes a fast and high accuracy iris localization algorithm for pigs based on low-cost acquisition equipment. Firstly, the pig iris image was denoised and binarized, then the Canny algorithm was improved to extract the edge information of the pig iris image, and then the inner and outer edges of the iris were located using the improved Hough circle transform and iterative reweighted least square method, so as to complete the localization of the pig iris. Experiments on 3 100 pig eye iris images showed that the accuracy rate of the iris was 96.19% and the average localization time was 342.50 ms. Compared with the DAUGMAN, WILDES and unit sector ring grey scale algorithms, the accuracy rate was improved by 10.49 percentage points, 8.67 percentage points and 3.61 percentage points, respectively, and the average localization time was reduced by 70.90, 81.50, 98.00 ms. This method could shorten the localization time while ensuring accuracy rate, and effectively improve the problem of poor effect of traditional iris positioning algorithm applied to pig iris, so as to lay an important foundation for the subsequent iris-based pig identification.

Key words: pig, iris localization, Canny algorithm, Hough circle transform, accuracy rate

中图分类号:

S818.9

刘伟东, 周素茵, 徐爱俊, 叶俊华. 生猪虹膜快速定位算法研究[J]. 浙江农业学报, 2024, 36(11): 2617-2626.

LIU Weidong, ZHOU Suyin, XU Aijun, YE Junhua. Research on fast localization algorithm of pig iris[J]. Acta Agriculturae Zhejiangensis, 2024, 36(11): 2617-2626.

图/表 11

图1 虹膜采集示意图

Fig.1 Schematic diagram of iris capture

图2 部分生猪的虹膜图像

Fig.2 Iris images of some pigs

图3 预处理结果 A,原图;B,中值滤波去噪;C,形态学开运算;D,形态学闭运算;E,自适应阈值二值化处理。

Fig.3 Pretreatment results A, Original figure; B, Median filter denoising; C, Morphological opening calculation; D, Morphological closed calculation; E, Adaptive threshold binarization process.

图4 改进的Hough圆变换取点原理图

Fig.4 Schematic diagram of the improved Hough circle transform to fetch points

图5 改进前后Canny算法边缘检测结果对比 A,改进前Canny算法边缘检测结果;B,改进后Canny算法边缘检测结果。

Fig.5 Comparison of edge detection results of Canny algorithm before and after improvement A, Edge detection results of Canny algorithm before improvement; B, Edge detection results of Canny algorithm after improvement.

图6 不同算法的定位试验结果 A,DAUGMAN算法定位结果;B,WILDES算法定位结果;C,单位扇环灰度算法定位结果;D,本文算法定位结果。

Fig.6 Localization test results of different algorithms A, DAUGMAN algorithm localization result; B, WILDES algorithm localization result; C, Gray value of unit sector area algorithm localization result; D, The algorithms of this paper localization result.

表1 各算法性能对比

Table 1 Performance comparison of algorithms %

算法 Algorithm	正确检测率 Accuracy rate	漏检率 Omissive rate	误检率 False rate
DAUGMAN	85.70	8.39	5.91
WILDES	87.52	7.84	4.64
单位扇环灰度算法	92.58	2.23	5.19
Gray value of unit sector area algorithm
本文算法	96.19	1.23	2.28
The algorithm in this paper

图7 本文方法与其他算法的定位时间对比

Fig.7 Comparison of the locating time of this paper’s method with other algorithms

表2 对比试验结果

Table 2 Results of comparative tests

算法名称 Algorithm name	正确检测率 Correct detection rate/%	内边缘平均定位时间 Average localization time for inner edges/ms	外边缘平均定位时间 Average localization time for outer edges/ms
最小二乘法The least squares method	86.29	203.91	215.83
Hough圆变换Hough’s circular transform	88.58	210.96	217.12
本文算法The algorithms in this paper	96.19	168.34	174.16

图8 部分非理想猪眼虹膜图像示例 A,光斑影响;B,睫毛遮挡;C,图像模糊。

Fig.8 Examples of some non-ideal pig iris images A, Spot effects; B, Eyelash masking; C, Blurred image.

图9 定位失败的猪眼虹膜图像示例

Fig.9 Example of a pig iris image with failed localization

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生猪虹膜快速定位算法研究

Research on fast localization algorithm of pig iris

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