Dianxin kexue (Jul 2022)
Feature enhancement and bilinear feature vector fusion for text detection of mobile industrial containers
Abstract
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.
