RETRACTED: Research on multi-task perception network of traffic scene based on feature fusion (Retracted Article)

Aiming at the problems of low precision and low real-time performance when deploying to embedded platforms in existing multi-task networks, this paper proposes a traffic scene multi-task perception network model (ETS YOLOP) based on feature fusion. Firstly, an Efficient Attention Control Aggregation Network Module (EACAN) is constructed to improve the real-time perception of the model, and the Space Pyramid Pool Fast Convolutional Module (SPPFCSPC) is used at the end of the backbone network to increase the receptive field. Finally, a Multiscale Convolution Transformer Fusion Module (CTFM) is designed in the task branch to better capture global information and rich context information. The experimental results show that compared with the YOLOP model, the ETS YOLOP model has a significant improvement in perception accuracy, 156% in real-time performance, 0.4% increase in mAP on the object detection task, 0.5% increase in mIoU on the drivable area segmentation task, and an 11.4% increase in accuracy on the lane detection task. In order to verify the real-time perception of the model on the embedded platform, the ETS YOLOP model is deployed on the Huawei MDC300F computing platform. Under the condition of the image input size of 640x640, the average frame rate can reach 55FPS, which can realize real-time perception on the embedded platform.