8 citations · 13 across the 5 of their papers we have counts for
5 papers · 1 filter
TwinMixing: A Shuffle-Aware Feature Interaction Model for Multi-Task Segmentation
Minh-Khoi Do, Huy Che, Dinh-Duy Phan +2
Accurate and efficient perception is essential for autonomous driving, where segmentation tasks such as drivable-area and lane segmentation provide critical cues for motion plannin…
R&D: Balancing Reliability and Diversity in Synthetic Data Augmentation for Semantic Segmentation
Huy Che, Dinh-Duy Phan, Duc-Khai Lam
Collecting and annotating datasets for pixel-level semantic segmentation tasks are highly labor-intensive. Data augmentation provides a viable solution by enhancing model generaliz…
TriLiteNet: Lightweight Model for Multi-Task Visual Perception
Quang-Huy Che, Duc-Khai Lam
Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiven…
TwinLiteNet+: An Enhanced Multi-Task Segmentation Model for Autonomous Driving
Quang-Huy Che, Duc-Tri Le, Minh-Quan Pham +2
Semantic segmentation is a fundamental perception task in autonomous driving, particularly for identifying drivable areas and lane markings to enable safe navigation. However, most…
TwinLiteNet: An Efficient and Lightweight Model for Driveable Area and Lane Segmentation in Self-Driving Cars
Quang Huy Che, Dinh Phuc Nguyen, Minh Quan Pham +1
Semantic segmentation is a common task in autonomous driving to understand the surrounding environment. Driveable Area Segmentation and Lane Detection are particularly important fo…