Traffic Sign Detection and Recognition under Complicated Illumination

In order to deal with the difficulty of traffic sign image detection and recognition under different illumination condition, an illumination image enhancement algorithm based on Retinex-Gamma is proposed. The algorithm is combined with Mask R-CNN, which is called Retinex-Gamma Mask R-CNN algorithm. Firstly, based on the illumination reflection imaging model, the image RGB space is transformed into HSV space, the V channel is processed by multi-scale Gaussian filtering to obtain the illumination component, the illumination component is used to extract the reflection component, and the reflection component is linearly optimized. Secondly, the two-dimensional Gamma function is adjusted by using the distribution characteristics of illumination components, and the optimized brightness components are obtained. Finally, the enhanced V channel is obtained by using the mixed space enhancement method to reconstruct the image. The ZCTSDB dataset used for the experiment has a total of 15 724 images and contains driving environments with different lighting. The experimental results show that compared with the standard Mask R-CNN, the average accuracy of Retinex-Gamma-Mask R-CNN algorithm for target detection of traffic signs is improved by 0.161%, and the average accuracy of instance segmentation of traffic signs is improved by 0.363%.