18 citations · 22 across the 15 of their papers we have counts for
10 papers · 1 filter
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver Assistance Systems
Hao Wu, Junzhou Chen, Ronghui Zhang +4
Object detection is a cornerstone of environmental perception in advanced driver assistance systems(ADAS). However, most existing methods rely on RGB cameras, which suffer from sig…
ABCDWaveNet: Advancing Robust Road Ponding Detection in Fog through Dynamic Frequency-Spatial Synergy
Ronghui Zhang, Dakang Lyu, Tengfei Li +7
Road ponding presents a significant threat to vehicle safety, particularly in adverse fog conditions, where reliable detection remains a persistent challenge for Advanced Driver As…
Lane Departure Accident Prevention in Foggy Conditions: A Prior-Guided Dynamic Feature Fusion Transformer Framework for Real-Time Lane Detection
Ronghui Zhang, Yuhang Ma, Tengfei Li +9
Lane departure accident prevention plays a critical role in enhancing road safety, and lane detection is a core technology to achieve this goal, especially under complex weather co…
YOLO-LLTS: Real-Time Low-Light Traffic Sign Detection via Prior-Guided Enhancement and Multibranch Feature Interaction
Ziyu Lin, Yunfan Wu, Yuhang Ma +5
Traffic sign detection is essential for autonomous driving and Advanced Driver Assistance Systems (ADAS). However, existing methods struggle to address the challenges of poor image…
YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion
Junzhou Chen, Heqiang Huang, Ronghui Zhang +4
Ensuring safety in both autonomous driving and advanced driver-assistance systems (ADAS) depends critically on the efficient deployment of traffic sign recognition technology. Whil…
AGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety
Ronghui Zhang, Shangyu Yang, Dakang Lyu +5
Road ponding, a prevalent traffic hazard, poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to se…
BA-Net: Bridge Attention in Deep Neural Networks
Ronghui Zhang, Runzong Zou, Yue Zhao +5
Attention mechanisms, particularly channel attention, have become highly influential in numerous computer vision tasks. Despite their effectiveness, many existing methods primarily…
GrabDAE: An Innovative Framework for Unsupervised Domain Adaptation Utilizing Grab-Mask and Denoise Auto-Encoder
Junzhou Chen, Xuan Wen, Ronghui Zhang +4
Unsupervised Domain Adaptation (UDA) aims to adapt a model trained on a labeled source domain to an unlabeled target domain by addressing the domain shift. Existing Unsupervised Do…
DSDFormer: An Innovative Transformer-Mamba Framework for Robust High-Precision Driver Distraction Identification
Junzhou Chen, Zirui Zhang, Jing Yu +5
Driver distraction remains a leading cause of traffic accidents, posing a critical threat to road safety globally. As intelligent transportation systems evolve, accurate and real-t…
BA-Net: Bridge Attention for Deep Convolutional Neural Networks
Yue Zhao, Junzhou Chen, Zirui Zhang +1
In recent years, channel attention mechanism has been widely investigated due to its great potential in improving the performance of deep convolutional neural networks (CNNs) in ma…