Road Semantic Segmentation and Traffic Object Detection Model Based on Encoder-Decoder CNN Architecture
Yih-Chen Wang, Chao-Wei Yu, Xiu-Ying Lu, Yen‐Lin Chen · 2022 IEEE International Conference on Consumer Electronics - Taiwan · 2022
Nowadays, there are lots of deep learning models being used, in the case of limited computing resources, the speed of performing object detection and semantic segmentation at the same time may encounter the problem of slowing down. To tackle this issue, we propose a multi-tasking learning model based on the Encoder-decoder CNN architecture, which merges the object detection and semantic segmentation models into one, thus could be trained with semantic segmentation task and object detection task at the same time, and applied on road and traffic object recognition in Taiwan's unique driving environment. Comparing to executing semantic segmentation and object detection models simultaneously, our proposed model has faster recognition speed and higher accuracy on Cityscapes dataset. The result shows that our proposed method can achieve faster recognition speed and maintain accuracy rate on an embedded platform of Nvidia Jetson TX2 with fewer computing resources.