Feature enhancement and bilinear feature vector fusion for text detection of mobile industrial containers - Haiyang HU; Zepin LI; Zhongjin LI (Dianxin kexue, 2022)
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.