Driven by deep learning techniques, perception technology in autonomous driving has
developed rapidly in recent years, enabling vehicles to accurately detect and interpret …

Today's deep learning methods focus on how to design the most appropriate objective
functions so that the prediction results of the model can be closest to the ground truth …

Industrial products typically lack defects in smart manufacturing systems, which leads to an
extremely imbalanced task of recognizing surface defects. With this imbalanced sample …

In the past years, YOLO-series models have emerged as the leading approaches in the area
of real-time object detection. Many studies pushed up the baseline to a higher level by …

Real-time multi-person pose estimation presents significant challenges in balancing speed
and precision. While two-stage top-down methods slow down as the number of people in the …

As agricultural applications are specialized, plant diseases are diverse, and there is a lack of
agricultural datasets, current plant disease identification performance is inadequate. In this …

Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-
time object detection owing to their effective balance between computational cost and …

Change detection is an essential and widely utilized task in remote sensing that aims to
detect and analyze changes occurring in the same geographical area over time, which has …

To keep the balance of precision and speed of unmanned aerial vehicles (UAVs) in
detecting insulator defects during power inspection, an improved insulator defect …

As the most fundamental scene understanding tasks, object detection and segmentation
have made tremendous progress in deep learning era. Due to the expensive manual …