基于轻量级YOLOv5开发构建汉字检测识别分析系统

汉字检测、字母检测、手写数字检测、藏文检测、甲骨文检测在我之前的文章中都有做过了，今天主要是因为实际项目的需要，之前的汉字检测模型较为古老了还使用的yolov3时期的模型，检测精度和推理速度都有不小的滞后了，这里要基于yolov5轻量级的模型来开发构建新版的目标检测模型，首先看下效果图：

接下来简单看下数据集情况：

YOLO格式标注文件截图如下：

实例标注内容如下所示：

17 0.245192 0.617788 0.038462 0.038462
6 0.102163 0.830529 0.045673 0.045673
16 0.894231 0.096154 0.134615 0.134615
4 0.456731 0.524038 0.134615 0.134615
15 0.367788 0.317308 0.269231 0.269231

VOC格式数据标注文件截图如下：

实例标注内容如下所示：

<annotation>
    <folder>DATASET</folder>
    <filename>0ace8eaf-8e86-488b-9229-95255c69158c.jpg</filename>
    <source>
        <database>The DATASET Database</database>
        <annotation>DATASET</annotation>
        <image>DATASET</image>
    </source>
    <owner>
        <name>YMGZS</name>
    </owner>    
    <size>
        <width>416</width>
        <height>416</height>
        <depth>3</depth>
    </size>
    <segmented>0</segmented>
    
    <object>        
        <name>17</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>214</xmin>
            <ymin>302</ymin>
            <xmax>230</xmax>
            <ymax>318</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>16</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>210</xmin>
            <ymin>67</ymin>
            <xmax>229</xmax>
            <ymax>86</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>18</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>260</xmin>
            <ymin>7</ymin>
            <xmax>274</xmax>
            <ymax>21</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>10</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>121</xmin>
            <ymin>103</ymin>
            <xmax>143</xmax>
            <ymax>125</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>11</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>296</xmin>
            <ymin>289</ymin>
            <xmax>352</xmax>
            <ymax>345</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>0</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>56</xmin>
            <ymin>132</ymin>
            <xmax>196</xmax>
            <ymax>272</ymax>
        </bndbox>
    </object>
    
    <object>        
        <name>0</name>
        <pose>Unspecified</pose>
        <truncated>0</truncated>
        <difficult>0</difficult>
        <bndbox>
            <xmin>213</xmin>
            <ymin>142</ymin>
            <xmax>353</xmax>
            <ymax>282</ymax>
        </bndbox>
    </object>
    
</annotation>

因为是主打轻量级网络，这里选择了也是最为轻量级的n系列的模型，最终训练得到的模型文件不足4MB大小，网络结构图如下所示：