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| Multi-task Sensing Algorithm for Driverless Vehicle Based on Feature Fusion |
| SUN Chuanlong1, ZHAO Hong1, CUI Xiangyu2, MU Liang1, XU Fuliang1, LU Laiwei1
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1. College of Mechanical and Electrical Engineering ,Qingdao University, Qingdao 266071, China;
2. Hisense Laoshan R&D Center, Qingdao 266104, China |
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Abstract In order to improve the utilization of hardware resources of driverless vehicle perception system, a multi-task driverless vehicle perception algorithm based on feature fusion is constructed. The improved CSPDarknet53 is used as the backbone network of the model, and multi-scale features are extracted and fused by constructing feature fusion network and feature fusion module. The detection of 7 common road objects and pixel-level segmentation of the driving area are taken as examples. Multi-task DaSNet (Detection and Segmentation Net) is designed for training and testing. In order to compare model performance, BDD100K data set is used to train YOLOv5s, Faster R-CNN and U-NET models, and comparative analysis is made on mAP, Dice coefficient and detection speed and other performance indicators. The results showed that DaSNet multi-task model′s mAP value is 0.5% and 4.2% higher than YOLOv5s and Faster RCNN, respectively, and the detection speed of 121FPS can be achieved on RTX2080Ti GPU. Compared with U-NET network, Dice value of segmentation in priority and non-priority drivable are 4.4% and 6.8% higher, showing an obvious improvement.
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Received: 07 November 2021
Published: 08 October 2023
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