Implementation of a Vision System for an Autonomous Railway Maintenance Vehicle: Track and Object Detection with YOLO, Neural Networks and Region Growing
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Warnicke, Albin
Jönsson, Jesper
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Railway infrastructure is often expensive to maintain. To improve efficiency and lower these costs, the use of
autonomous railway vehicles for such maintenance has begun to be explored. A railway vehicle requires several
components to achieve complete automation, including systems for navigation, decision-making, and sensors
such as cameras. This project aims to develop the vision system used by an autonomous track trolley under
development at Chalmers University of Technology. The proposed vision system can detect railway tracks and
switches by a region-growing algorithm based on the image intensity gradient. Object detection is achieved
by the use of a YOLOv4-tiny neural network and is developed to detect persons, vehicles, railway signs and
signals, road crossings and catenary support poles. The signal and speed sign messages are further classified by
additional convolutional neural networks. The vision system is implemented as a ROS node on a single-board
computer, a NVIDIA Jetson Nano, and is running in real-time at up to 15 FPS.
The vision system is accurate and robust enough to be used as a prototype in simple environments. The
track that the vehicle is traveling on was detected in 98.4 % of the evaluated video frames, with the sidetracks
correctly identified in 70-80 % of the time. Several of the considered objects were detected with 90-100 %
accuracy, for example vehicles and road crossings. Other objects, particularly railway switches and incoming
tracks, were however only correctly recognized in about 60 % of their occurrences. Signals and speed signs were
detected with high accuracy.
Some features can be improved or added before the vision system can be applied to a complete autonomous
railway vehicle. The main limitation of the implemented object detection is the lack of large training datasets.
With more available video data, datasets with an increased number of labeled objects and greater diversity could
be created. Utilizing the full capabilities of larger datasets would eventually require the use of more complex
neural networks. The currently used hardware however limits the possible methods to simpler algorithms.
The track detection algorithm can serve as a base for further improvement, with the region growing based on
the image intensity gradients not being robust enough to handle large variations in lighting and environment
conditions. An approach with semantic segmentation neural networks is instead suggested to achieve robust
track detection.
Beskrivning
Ämne/nyckelord
Autonomous vehicles , Railway , Object detection , Computer vision , Track detection , Machine learning , Arti cial neural networks , Convolutional neural networks , YOLO , You Only Look Once , Region growing