【摘要】 针对农资图像中文本的检测速度慢并且缺乏移动端的应用等问题,基于农资图像数据集,提出了一种基于Ghost模块的农资图像文本检测算法,该算法对DB网络进行改进,使用MobileNetv2网络来提取基础特征,引入多尺度特征融合模块来获得多层之间的特征融合,并采用可微分二值化后处理算法预测文本,使其能够快速地检测农资图像中的文本。该算法在农资图像数据集上的准确率基本达到了主流算法的标准,检测速度达18.6 img/s,参数量为2.99 M,具备轻量级的特征,将此算法部署到移动端设备上并成功运行。更多还原

【Abstract】 In response to problems such as slow detection speed of text in agricultural materials image and lack of mobile applications,based on the agricultural materials image dataset,a Ghost module-based text detection algorithm for agricultural materials image was proposed,which improved the DB network,used the MobileNetv2 network to extract the base features,introduced a multi-scale feature fusion module to obtain feature fusion between multiple layers,and used a differentiable binary post-processing algorithm to predict the text,making it possible to quickly detect the text in agricultural materials image. The accuracy of the algorithm on the agricultural materials image dataset was basically up to the standard of mainstream algorithms,with a detection speed of 18.6 img/s and a census count of 2.99 M,with lightweight features,and the algorithm was deployed to mobile devices and ran successfully.更多还原