Thesis Details
Rozpoznání hranic jízdního pruhu v záběrech palubní kamery
This paper talks about lane detection. Specifically custom generator of synthetic images, usage during training of neural networks, testing on convolutional neural network (CNN) UNet model and possibilities of extension of this model to SALMnet (Structure-Aware Lane Marking Detection Network) via addding SGCA module (semantic-guided channel attention) and PDC module (pyramid deformable convolution). Training results from synthetic datasets show very accurate results, reaching around 95\,\% in accuracy (even 99\,\% for easier images). Trainings with real datasets show lower accuracy, depending on the difficulty of the dataset itself. TuSimple has easier and clearer images and reaches about 62\,\%. CuLane is much more complex and results show accuracy around 37\,\%.
generator, computer learning, lane detection, neural network, synthetic dataset, UNet
Bařina David, Ing., Ph.D. (DCGM FIT BUT), člen
Burget Radek, doc. Ing., Ph.D. (DIFS FIT BUT), člen
Češka Milan, doc. RNDr., Ph.D. (DITS FIT BUT), člen
Mrázek Vojtěch, Ing., Ph.D. (DCSY FIT BUT), člen
@bachelorsthesis{FITBT24520, author = "David Fridrich", type = "Bachelor's thesis", title = "Rozpozn\'{a}n\'{i} hranic j\'{i}zdn\'{i}ho pruhu v z\'{a}b\v{e}rech palubn\'{i} kamery", school = "Brno University of Technology, Faculty of Information Technology", year = 2022, location = "Brno, CZ", language = "czech", url = "https://www.fit.vut.cz/study/thesis/24520/" }