Unification of road scene segmentation strategies using multistream data and latent space attention

DATA AVAILABILITY STATEMENT : Two datasets are references in this paper. The Cityscapes dataset is available in the Cityscapes web repository [21]. The CARLA dataset was custom-recorded from the CARLA simulator [44] and can be obtained from the first author upon request. The main training scripts that were used to create the road scene segmentation model will be made available with this paper.Road scene understanding, as a field of research, has attracted increasing attention in recent years. The development of road scene understanding capabilities that are applicable to realworld road scenarios has seen numerous complications. This has largely been due to the cost and complexity of achieving human-level scene understanding, at which successful segmentation of road scene elements can be achieved with a mean intersection over union score close to 1.0. There is a need for more of a unified approach to road scene segmentation for use in self-driving systems. Previous works have demonstrated how deep learning methods can be combined to improve the segmentation and perception performance of road scene understanding systems. This paper proposes a novel segmentation system that uses fully connected networks, attention mechanisms, and multiple-input data stream fusion to improve segmentation performance. Results show comparable performance compared to previous works, with a mean intersection over union of 87.4% on the Cityscapes dataset.The Centre for Connected Intelligence (CCI) at the University of Pretoria (UP), and the APC was partially funded by CCI and UP.https://www.mdpi.com/journal/sensorsam2024Electrical, Electronic and Computer EngineeringNon

Machine Learning For Object Recognition and Scene Analysis

Learning is a critical research field for autonomous computer vision systems. It can bring solutions to the knowledge acquisition bottleneck of image understanding systems. Recent developments of machine learning for computer vision are reported in this paper. We describe several different approaches for learning at different levels of the image understanding process, including learning 2-D shape models, learning strategic knowledge for optimizing model matching, learning for adaptative target recognition systems, knowledge acquisition of constraint rules for labelling and automatic parameter optimization for vision systems. Each approach will be commented and its strong and weak points will be underlined. In conclusion we will suggest what could be the "ideal" learning system for vision