Multi Feature Fusion for Road Panoramic Driving Detection Based on YOLOP-L
In recent years,traffic image detection technology from the driver's perspective has become an important research di-rection in the field of transportation,and extracting various features such as vehicles,roads,and traffic signs has become an urgent task for drivers to understand the diversity of road information.Previous studies have made significant progress in feature extrac-tion for single class object detection.However,these studies cannot be well applied to other feature detection with significant differences,and the accuracy of individual feature detection will be lost during fusion training.In response to the diverse and com-plex road information within the driver's field of view,this paper proposes a detection model YOLOP-L based on multi feature fusion training.It can simultaneously fuse and train multiple different feature traffic targets,while ensuring the accuracy of indi-vidual detection tasks.The results indicate that YOLOP-L can effectively solve the problems of insufficient detection accuracy and missing segmentation in complex scenes on the challenging BDD100K dataset,improving the accuracy and robustness of vehicle recognition,lane line detection,and joint training of road driving areas.Finally,comparative experiments show that YOLOP-L runs faster than the original YOLOP network.The recall rate increases by 2.2％under the vehicle target detection task.In the lane detection task,the accuracy improves by 2.8％,and the IoU value of the lane line decreases by 2.45％compared to the Hy-bridNets network,but increases by 1.95％compared to the YOLOP-L network.Its overall detection performance improves by 1.1％under the task of driving area segmentation.The results indicate that YOLOP-L can effectively solve the problems of insuffi-cient detection accuracy and missing segmentation in complex scenes on the challenging BDD100K dataset,improving the accuracy and robustness of vehicle recognition,lane line detection,and joint training of road driving areas.
Panoramic drivingMulti featurefusionVehicle inspectionTravelable area detectionLane line detectionBidirectional feature pyramid network
NETL
NSTL
万方数据
