Брянск, Брянская область, Россия
ГРНТИ 50.07 Теоретические основы вычислительной техники
ББК 3297 Вычислительная техника
This paper presents an approach to the unmanned control of a wheeled robot, which includes recognition of road infrastructure objects, recognition of continuous and intermittent road markings, generation of control signals. Recognition of road infrastructure objects is carried out using a neural network that generates a segmented image. After that, the segmented image is identified with the found objects, including the roadway, which is used by the road marking recognition subsystem searching for continuous and intermittent lines using the computer vision library. On the basis of the information received from the considered subsystems control commands are generated indicating the direction of movement and speed. The algorithm was developed on a 1:18 scale model of the city infrastructure, where a wheeled robot simulated as a car.
neural network, object localization, image segmentation, Pyramid Scene Parsing Network, wheeled robot, road markingrecognition
1. A. Buyval, M. Lyubimov, A. Gabdullin "Road signdetection and localization based on camera and lidardata" //The 11th International Conference on MachineVision (ICMV 2018) - Munich, Germany 2018
2. Cordts M., Omran M., Ramos S. ,Rehfeld T. ,EnzweilerM. , Benenson R., Franke U., Roth S., Schiele B. “TheCityscapes Dataset for Semantic Urban SceneUnderstanding”/ M. Coordts. 2016г.
3. Hengshuang Z., Jianping S., Xiaojuan Q., Xiaogang W.,Jiaya J.” Pyramid Scene Parsing Network”/ Z.Hengshuang. CVPR 2017г.
4. Md. S. Hossain, Z. Hyder “Traffic Road Sign Detectionand Recognition for Automotive Vehicles”,International Journal of Computer Applications (2015)
5. Kaiming H, Xiangyu Z ,Shaoqing R ,Jian S “DeepResidual Learning for Image Recognition” / H.Kaiming, Z. Xiangyu, R. Shaoqing, S Jian - MicrosoftResearch 2015г
6. J. Long, E. Shelhamer, T. Darrell "Fully ConvolutionalNetworks for Semantic Segmentation" // CVPR 2015
7. A. Møgelmose, M. M. Trivedi, T. B. Moeslund, "Visionbased Traffic Sign Detection and Analysis forIntelligent Driver Assistance Systems: Perspectives andSurvey, "IEEE Transactions on IntelligentTransportation Systems, (2012).
8. A. D. Pon, O. Andrienko, A. Harakeh, S. L. Waslander "A Hierarchical Deep Architecture and Mini-BatchSelection Method For Joint Traffic Sign and LightDetection" IEEE 15th Conference on Computer andRobot Vision (2018)
9. Duckietown [Electronic resource] - URL:https://www.duckietown.org, available.
10. Formula Pi [Electronic resource]. - URL:https://www.formulapi.com, available.
