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.
孙传龙, 赵红, 崔翔宇, 牟亮, 徐福良, 路来伟. 基于特征融合的无人驾驶多任务感知算法[J]. 复杂系统与复杂性科学, 2023, 20(3): 103-110.
SUN Chuanlong, ZHAO Hong, CUI Xiangyu, MU Liang, XU Fuliang, LU Laiwei. Multi-task Sensing Algorithm for Driverless Vehicle Based on Feature Fusion[J]. Complex Systems and Complexity Science, 2023, 20(3): 103-110.
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