特征增强和双线性特征向量融合的移动端工业货箱文本检测

在实际工业环境下，光线昏暗、文本不规整、设备有限等因素，使得文本检测成为一项具有挑战性的任务。针对此问题，设计了一种基于双线性操作的特征向量融合模块，并联合特征增强与半卷积组成轻量级文本检测网络RGFFD（ResNet18+GhostModule+特征金字塔增强模块（feature pyramid enhancement module， FPEM）+ 特征融合模块（feature fusion module，FFM）+可微分二值化（differenttiable binarization，DB））。其中，Ghost模块内嵌特征增强模块，提升特征提取能力，双线性特征向量融合模块融合多尺度信息，添加自适应阈值分割算法提高DB模块分割能力。在实际工厂环境下，采用嵌入式设备UP2 board对货箱编号进行文本检测，RGFFD检测速度达到6.5 f/s。同时在公共数据集ICDAR2015、Total-text上检测速度分别达到39.6 f/s和49.6 f/s，在自定义数据集上准确率达到88.9%，检测速度为30.7 f/s。

Feature enhancement and bilinear feature vector fusion for text detection of mobile industrial containers

In the real factory environment, due to factors such as dim light, irregular text, and limited equipment, text detection becomes a challenging task.Aiming at this problem, a feature vector fusion module based on bilinear operation was designed and combined with feature enhancement and semi-convolution to form a lightweight text detection network RGFFD (ResNet18 + Ghost Module + FPEM(feature pyramid enhancement module)) + FFM(feature fusion module) + DB (differentiable binarization)).Among them, the Ghost module was embedded with a feature enhancement module to improve the feature extraction capability, the bilinear feature vector fusion module fused multi-scale information, and an adaptive threshold segmentation algorithm was added to improve the segmentation capability of the DB module.In the real industrial environment, the RGFFD detection speed reached 6.5 f/s, when using the embedded device UP2 board for text detection of container numbers.At the same time, the detection speed on the public datasets ICDAR2015 and Total-text reached 39.6 f/s and 49.6 f/s, respectively.The accuracy rate on the custom dataset reached 88.9%, and the detection speed was 30.7 f/s.