3 citations · 3 across the 4 of their papers we have counts for
4 papers
Federated Learning-based Semantic Segmentation for Lane and Object Detection in Autonomous Driving
Gharbi Khamis Alshammari, Ahmad Abubakar, Nada M. O. Sid Ahmed +1
Autonomous Vehicles (AVs) require precise lane and object detection to ensure safe navigation. However, centralized deep learning (DL) approaches for semantic segmentation raise pr…
Multi-Model Learning for Real-Time Automotive Semantic Foggy Scene Understanding via Domain Adaptation
Naif Alshammari, Samet Akcay, Toby P. Breckon
Robust semantic scene segmentation for automotive applications is a challenging problem in two key aspects: (1) labelling every individual scene pixel and (2) performing this task…
Competitive Simplicity for Multi-Task Learning for Real-Time Foggy Scene Understanding via Domain Adaptation
Naif Alshammari, Samet Akcay, Toby P. Breckon
Automotive scene understanding under adverse weather conditions raises a realistic and challenging problem attributable to poor outdoor scene visibility (e.g. foggy weather). Howev…
Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation
Naif Alshammari, Samet Akçay, Toby P. Breckon
Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) perform…