Detection of Road Surface Identifiers Based on Deep Learning
Feng Zhang, Xiaoyu Wu, Chaonan Gu · 2019
Road markings are an important guarantee for road infrastructure and driving safety. In recent years, the rapid development of autonomous driving technology, and the identification ofroad identifiers has become an indispensable part of this technology. The traditional target detection algorithm in computer vision has poor regional selectivity, high time complexity and poor feature robustness. The deep learning algorithm can effectively solve these problems. At present, deep learning is widely used in audio data and images, and it is feasible and meaningful to identify commonroad identifiers. Based on the self-builtroad marking database, in view of the problems existing in the currentroad marking detection research, the latest deep learning method is used to detect the six common types ofroad signs such as the right turn arrow, the straight right turn arrow and the crosswalk, and the SSD is trained. Modifying the four target detection models of focal loss SSD, FSSD and R-FCN and testing the road surface. Through data analysis and comparison, the following conclusions can be drawn: R-FCN has higher accuracy, but slower speed; The accuracy of SSD model is low whilst the detection speed is fast. The accuracy of the SSD model after modifying the focal loss is improved compared with the SSD model. The accuracy of the FSSD model is between R-FCN and SSD, and it can also maintain a faster speed. Comparing the experimental results, the R-FCN model with 18-layer depth residual network is the best for road marking detection.