Developing an open-source, low-cost, lightweight educational robotics platform with open software and hardware
Researching adaptive deep perception methods for autonomous surface vehicles (USVs), focusing on general obstacle detection, long-term tracking with re-identification, implicit hazardous area detection, and multi-sensor fusion
Developing autonomous navigation policies for mobile robots using deep reinforcement learning, trained entirely in simulation and deployed on real robots
Designing novel deep architectures for visual surface defect inspection, capable of detecting large defects like cracks and smooth deformations such as dents on reflective surfaces
Proposing D3S, a discriminative single-shot segmentation-based tracker that bridges visual object tracking and video object segmentation using two complementary geometric target models
Developing maritime perception models using modalities beyond the visible spectrum (e.g., thermal imaging) for river navigation
Building robust computer vision methods for USV navigation in uncontrolled environments, including monocular/stereo obstacle detection, efficient marine visual tracking, and sensor-fusion-based environment representation
Designing long-term trackers with target re-detection capabilities for applications in surveillance, smart cameras, speaker-following in video conferencing, etc.
Developing a Mask R-CNN-based method for large-scale traffic sign detection and recognition using the DFG dataset (7,000+ images, 200 traffic sign categories)