不知道大家平时在路上走的时候或者在小区的时候有没有遇上过遛狗不牵绳子的行为,我在实际生活里面可是没少遇到过,有时候特别大的一只狗就这么冲过来,主人却还无动于衷,揍他的心都有了,这种行为的确是很不文明,希望都能互相理解遵守文明生活规则。
闲话就说这么多了,本文主要是基于yolov7来构建不文明行为检测识别分析系统,以期来自动化地检测识别遛狗未牵绳子的不文明行为,首先看下效果图:
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接下来看下数据集情况:
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YOLO格式标注数据如下:
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实例标注内容如下:
0 0.524194 0.843548 0.248387 0.306452
0 0.616129 0.040323 0.077419 0.080645
1 0.783871 0.340323 0.4 0.674194
1 0.31129 0.332258 0.622581 0.6
0 0.232258 0.08871 0.219355 0.170968
VOC格式标注数据如下:
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实例标注内容如下:
<?xml version='1.0' encoding='UTF-8'?>
<annotation>
<filename>5d21afa0-1a30-489d-8e2c-5af9cdb88a52.jpg</filename>
<object_num>8</object_num>
<size>
<width>1000</width>
<height>750</height>
</size>
<object>
<name>DOG</name>
<difficult>0</difficult>
<bndbox>
<xmin>235</xmin>
<ymin>543</ymin>
<xmax>355</xmax>
<ymax>724</ymax>
</bndbox>
</object>
<object>
<name>ROPE</name>
<difficult>0</difficult>
<bndbox>
<xmin>403</xmin>
<ymin>85</ymin>
<xmax>596</xmax>
<ymax>724</ymax>
</bndbox>
</object>
<object>
<name>DOG</name>
<difficult>0</difficult>
<bndbox>
<xmin>689</xmin>
<ymin>494</ymin>
<xmax>875</xmax>
<ymax>705</ymax>
</bndbox>
</object>
<object>
<name>DOG</name>
<difficult>0</difficult>
<bndbox>
<xmin>561</xmin>
<ymin>471</ymin>
<xmax>719</xmax>
<ymax>656</ymax>
</bndbox>
</object>
<object>
<name>ROPE</name>
<difficult>0</difficult>
<bndbox>
<xmin>410</xmin>
<ymin>126</ymin>
<xmax>572</xmax>
<ymax>422</ymax>
</bndbox>
</object>
<object>
<name>DOG</name>
<difficult>0</difficult>
<bndbox>
<xmin>121</xmin>
<ymin>543</ymin>
<xmax>271</xmax>
<ymax>724</ymax>
</bndbox>
</object>
<object>
<name>DOG</name>
<difficult>0</difficult>
<bndbox>
<xmin>383</xmin>
<ymin>538</ymin>
<xmax>482</xmax>
<ymax>731</ymax>
</bndbox>
</object>
<object>
<name>DOG</name>
<difficult>0</difficult>
<bndbox>
<xmin>563</xmin>
<ymin>479</ymin>
<xmax>841</xmax>
<ymax>697</ymax>
</bndbox>
</object>
</annotation>
关于融合RepVGG的可以参考我前面的文章:
《基于轻量级YOLOV5融合RepVGG的电梯内电动车检测识别分析系统》
本质思想都是一样的,这里就简单看下了:
BackBone加入RepVGGBlock
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Head加入RepConv
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默认100次epoch迭代计算,结果详情如下:
LABEL可视化:
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batch检测实例:
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训练可视化:
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F1曲线:
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PR曲线:
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混淆矩阵:
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