WCSE

Training Instance Segmentation and Lane Detection Models in One Network Architecture

Abstract— A new network architecture with a novel training method is proposed in this paper which can achieve two tasks of road defects instance segmentation and lane detection. It is composed of a backbone and two independent output branches for instance segmentation and lane detection. The experiments are conducted on new datasets collected by us. Through our method of alternately training two network branches while continuously reducing the learning rate, it can be found that the accuracy of our two branches can be similar with the accuracy training with two different models. This shows the effectiveness of our training method. Furthermore, our method can reduce model memory.

Index Terms— instance segmentation, lane detection, alternately training, road defects

Dunan Ye
School of Data Science Engineering, East China Normal University, CHINA
Jun Guo
Information Technology Services, East China Normal University, CHINA
Youguang Chen
School of Data Science Engineering, East China Normal University, CHINA

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Cite: Dunan Ye, Jun Guo, Youguang Chen, "Training Instance Segmentation and Lane Detection Models in One Network Architecture ," 2021 The 11th International Workshop on Computer Science and Engineering (WCSE 2021), pp. 101-106, Shanghai, China, June 19-21, 2021.