python基于轻量级YOLOv5的生猪检测+状态识别分析系统

在我之前的一篇文章中有过生猪检测盒状态识别相关的项目实践，如下：

《Python基于yolov4实现生猪检测及状态识》

感兴趣的话可以自行移步阅读，这里主要是基于同样的技术思想，将原始体积较大的yolov4模型做无缝替换，使用当下比较优秀的轻量级yolov5s模型来实现目标检测，后续基于状态识别模型实现生猪状态的识别，首先看下效果图，如下所示：

 简单看下数据集：

 YOLO格式标注文件如下所示：

 实例标注内容如下所示：

0 0.062744 0.558594 0.046387 0.16276
0 0.077637 0.701497 0.0625 0.126953
0 0.107422 0.805664 0.053711 0.087891
0 0.129883 0.798503 0.063477 0.138672
0 0.151367 0.811198 0.073242 0.123698
0 0.22876 0.842773 0.085449 0.115234
0 0.283936 0.794922 0.066895 0.227865
0 0.333496 0.773438 0.06543 0.197917
0 0.362793 0.812826 0.078125 0.166016
0 0.394043 0.848958 0.108398 0.167969
0 0.468994 0.878255 0.131348 0.105469
0 0.720459 0.733398 0.068848 0.19987
0 0.86499 0.628255 0.096191 0.091146
0 0.922607 0.434245 0.040527 0.164062
0 0.87915 0.301107 0.046387 0.146484
0 0.907715 0.297852 0.035156 0.120443
0 0.870117 0.166992 0.047852 0.108724
0 0.829102 0.145182 0.058594 0.097656
0 0.79126 0.264974 0.112793 0.135417
0 0.684326 0.127279 0.104004 0.078776
0 0.668213 0.068685 0.10498 0.064453
0 0.616699 0.142578 0.104492 0.174479
0 0.49292 0.151042 0.162598 0.098958
0 0.437256 0.417643 0.202637 0.212891
0 0.387207 0.329753 0.104492 0.210286
0 0.300049 0.403971 0.069824 0.222005
0 0.195312 0.514974 0.12207 0.227865
0 0.222168 0.451497 0.092773 0.133464

VOC格式标注文件如下所示：

 实例标注数据如下所示：

<annotation>
    <folder>DATASET</folder>
    <filename>images/20190621141536.jpg</filename>
    <source>
        <database>The DATASET Database</database>
        <annotation>DATASET</annotation>
        <image>DATASET</image>
    </source>
    <owner>
        <name>YMGZS</name>
    </owner>    
    <size>
        <width>2048</width>
        <height>1536</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    
    <object>        
        <name>pig</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>775</xmin>
            <ymin>1268</ymin>
            <xmax>1072</xmax>
            <ymax>1406</ymax>
        </bndbox>
    </object>
    
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        <name>pig</name>
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        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
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            <ymax>1434</ymax>
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    </object>
    
    <object>        
        <name>pig</name>
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        <truncated>0</truncated>
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            <ymax>1333</ymax>
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    </object>
    
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        <name>pig</name>
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        <truncated>0</truncated>
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        <bndbox>
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            <ymin>1197</ymin>
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            <ymax>1366</ymax>
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    <object>        
        <name>pig</name>
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        <truncated>0</truncated>
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            <xmax>399</xmax>
            <ymax>1302</ymax>
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    <object>        
        <name>pig</name>
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            <ymax>1311</ymax>
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    <object>        
        <name>pig</name>
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    <object>        
        <name>pig</name>
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    <object>        
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    </object>
    
</annotation>

默认使用轻量级的yolov5s模型来进行模型的开发，默认训练100次epoch，结果详情如下所示：

【F1值曲线】

 【PR曲线】

 【Precision和Recall曲线】

 数据可视化：

 Batch计算实例：

 可视化界面推理实例如下：

 目标检测+状态识别在界面中做了集成实现。