没有合适的资源?快使用搜索试试~ 我知道了~
资源推荐
资源详情
资源评论
题目:
AI 动物识别工具的设计与实现
学生姓名:
学 号:
所属院系:
专 业:
班 级:
指导老师:
日 期:
年 月 日
I
摘 要
随着硬件技术的不断完善,现在的图像获取技术、摄像技术都在不断的向更
加方便快捷的方向发展,人们在日常生活中实现照片图像的获取非常的便捷,且
获取的成本越来越低,数字化的相机等监控设备的应用也越来越广泛,在为科学
研究、交通管理、物流监控等方面都着非常成熟的应用。而在面向自然生态领域
中,通过利用图像监控技术来对自然界中的野生动物进行监控和记录,能够更好
的了解到野生动物的生存现状,并且可以熟悉地区内动物的迁徙习惯,在对野生
动物的保护方面、对于大自然的保障方面都能够起到非常好的作用。而现在在自
然科学领域中,通过大量的相机使用可以采取海量的野生动物图像数据,通过对
数据的整理可以总结出非常多的与野生动物生活习惯相关的数据,现在科学研究
者所面临的问题是如何将图像数据快速的、低成本的转化为数据,从而为研究者
提供可视化的数据分析。借助于神经网络的快速发展,现在通过计算机来对图像
进行识别应用非常成熟,通过对计算机的训练,可以让计算机能够快速的进行图
像的分类、识别、检测等功能实现。在动物的图像识别上,实际在当下的应用发
展过程中仍然存在一些欠缺,特别是在图像监督标签、数据均衡处理等方式上存
在一些研究上的缺陷,本次是通过利用 OpenCV、yolov5 技术, python 语言等
技术来进行一次 AI 动物识别技术的开发应用,通过本次的开发可以在专项用于
动物识别方面建立一个专业化的应用平台,完成计算机对于动物的正确、快速的
识别功能的实现。
关 键 词:深度学习;动物识别;AI;OpenCV
II
ABSTRACT
With the continuous improvement of hardware technology, image acquisition
technology and camera technology are constantly developing in a more convenient and
fast direction. People achieve very convenient acquisition of photo images in their daily
life, and the cost of acquisition is getting lower and lower. Digital cameras and other
monitoring devices are also being used more and more widely. They have very mature
applications in scientific research, traffic management, logistics monitoring and so on.
In the field of natural ecology, by using image monitoring technology to monitor and
record wildlife in the natural world, we can get a better understanding of the survival
status of wildlife, and can be familiar with the migration habits of animals in the region,
which can play a very good role in wildlife protection and natural protection. Now in
the field of natural science, through the use of a large number of cameras, a large
amount of wildlife image data can be taken. By collating the data, a large amount of
data related to wildlife habits can be summarized. The problem faced by scientific
researchers is how to convert the image data into data quickly and cheaply, so as to
provide visual data analysis for researchers. With the rapid development of the neural
network, the application of image recognition by computer is very mature now. With
the training of computer, the computer can quickly perform image classification,
recognition, detection and other functions. In animal image recognition, there are still
some deficiencies in the current application development process, especially in image
supervision labels, data balance processing and other ways. This time, we develop and
apply AI animal recognition technology by using OpenCV, yolov5 technology, Python
language and other technologies. With this development, a specialized application
platform for animal recognition can be established to complete the correct and fast
recognition function of computers for animals.
Keywords: In-depth learning; Animal identification; AI; OpenCV
III
目 录
1 绪论 ............................................................................................................................5
1.1 研究背景..............................................................................................................5
1.2 研究现状..............................................................................................................5
1.3 研究的意义..........................................................................................................6
1.4 开发的技术介绍..................................................................................................6
1.4.1 Python 技术....................................................................................................6
1.4.2 Django 框架 ...................................................................................................7
1.4.3 MySQL 数据库..............................................................................................7
1.4.4 B/S 结构 .........................................................................................................7
1.4.5 OpenCV 技术.................................................................................................8
2 深度学习的算法研究 ................................................................................................9
2.1 动物图像数据集..................................................................................................9
2.2 卷积神经网络......................................................................................................9
2.3 基于深度学习的野生动物识别..........................................................................9
2.4 YOLO 系列算法 ................................................................................................10
3 基于 AI 动物识别技术的需求分析........................................................................11
3.1 需求设计............................................................................................................11
3.2 可行性分析........................................................................................................11
3.2.1 技术可行性 .................................................................................................11
3.2.2 经济可行性 .................................................................................................12
3.2.3 操作可行性 .................................................................................................12
3.3 其他功能需求分析............................................................................................12
4 系统设计 ..................................................................................................................13
4.1 系统的功能模块设计........................................................................................13
4.2 数据库的设计....................................................................................................13
5 系统的实现 ..............................................................................................................15
5.1 系统的登录模块设计........................................................................................15
5.2 系统的首页实现................................................................................................15
5.3 图片识别的功能实现........................................................................................16
5.4 图片管理功能的实现........................................................................................17
5.5 图片分析功能的实现........................................................................................18
6 系统的测试 ..............................................................................................................19
6.1 测试的目的........................................................................................................19
6.2 测试的内容........................................................................................................19
6.3 测试的结果........................................................................................................19
IV
7 结论与展望 ..............................................................................................................20
7.1 结论....................................................................................................................20
7.2 展望....................................................................................................................20
参考文献 ......................................................................................................................21
致谢 ..............................................................................................................................23
剩余23页未读,继续阅读
资源评论
苹果牛顿吃
- 粉丝: 19
- 资源: 2791
上传资源 快速赚钱
- 我的内容管理 展开
- 我的资源 快来上传第一个资源
- 我的收益 登录查看自己的收益
- 我的积分 登录查看自己的积分
- 我的C币 登录后查看C币余额
- 我的收藏
- 我的下载
- 下载帮助
最新资源
- 单家独院式农房户型设计水电图.dwg
- STC12C5A60S2单片机+LCD12864俄罗斯方块源码程序实例源码KEIL C51工程文件.zip
- 编程IDE等宽字体,不错的
- FlowGeek FlowGeek是基于MVP架构的、遵循Material Design设计规范的开源中国社区客户端
- 单家独院式图纸农房户型设计90平09.20-t3.dwg
- 使用HttpURLConnect实现的文件下载器
- STM32驱动OLED1.54寸OLED驱动代码
- STC12C5A60S2单片机开发LCD12864液晶显示正弦函数实例源码KEIL C51工程文件.zip
- SI9933BDY-T1-E3-VB一款SOP8封装2个P-Channel场效应MOS管
- C++开发基于ROS实现多差速无人车编队控制源码+使用说明+详细注释 (期末大作业)
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
安全验证
文档复制为VIP权益,开通VIP直接复制
信息提交成功