Scene recognition under special traffic conditions based on deep multi-task learning

Xiaochang Hu, Xin Xu, Yongqian Xiao, Hongjun Chen, Hongjia Zhang
2020 The Journal of Engineering  
Traffic scene recognition under special conditions is one of the most promising yet challenging tasks for autonomous driving systems. This study presents a deep multi-task classification framework for scene recognition involving special traffic conditions. The framework incorporates four learning tasks where the recognition of special traffic scenes is the chief task and the time of occurrence (daytime or night-time), the weather type and the road attribute are the three auxiliary tasks for
more » ... oving the recognition performance. The four tasks share the feature map generated by a convolutional neural network followed by task-specific sub-networks which are merged in the end via a joint loss function. Moreover, a small dataset of typical special traffic conditions was built for training and testing the recognition model. Experimental results demonstrate that the proposed framework significantly improves the accuracy of scene recognition under special traffic conditions.
doi:10.1049/joe.2019.1191 fatcat:25qvpe2ur5ftdijk6qvf4kr3wq