Area of research
Computer Vision and Pattern Recognition · Artificial Intelligence
Research interest
Research interests include Computer science, Artificial intelligence, Feature (linguistics), Computer vision, Anomaly detection, and Pattern recognition (psychology).
MFNet: Multi-scale feature enhancement networks for wheat head detection and counting in complex scene
BiReNet: Bilateral Network with Feature Aggregation and Edge Detection for Remote Sensing Images Road Extraction
A survey of deep learning-based object detection methods in crop counting
YOLOF-F: you only look one-level feature fusion for traffic sign detection
Video Anomaly Detection Based on Attention Mechanism
ADD: An automatic desensitization fisheye dataset for autonomous driving
ST-YOLOX: a lightweight and accurate object detection network based on Swin Transformer
PVDet: Towards pedestrian and vehicle detection on gigapixel-level images
MTSDet: multi-scale traffic sign detection with attention and path aggregation
SARNet: Spatial Attention Residual Network for pedestrian and vehicle detection in large scenes
FE-CSP: a fast and efficient pedestrian detector with center and scale prediction
Faiad: Feature Adaptive-Based Image Anomaly Detection
Faiad: Feature Adaptive-Based Image Anomaly Detection
FAIAD: Feature Adaptive-based Image Anomaly Detection