Classical machine learning assumes that the training and test sets come from the same
distributions. Therefore, a model learned from the labeled training data is expected to …

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of
ground transportation. It is anticipated that ordinary vehicles will one day be replaced with …

Traffic sign detection is a challenging task for the unmanned driving system, especially for
the detection of multi-scale targets and the real-time problem of detection. In the traffic sign …

Object detection is a fundamental visual recognition problem in computer vision and has
been widely studied in the past decades. Visual object detection aims to find objects of …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …

Small object detection is a challenging problem in computer vision. It has been widely
applied in defense military, transportation, industry, etc. To facilitate in-depth understanding …

In this paper, we present a deep autoencoder based energy method (DAEM) for the
bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher …

Medical imaging plays a significant role in different clinical applications such as medical
procedures used for early detection, monitoring, diagnosis, and treatment evaluation of …

Due to object detection's close relationship with video analysis and image understanding, it
has attracted much research attention in recent years. Traditional object detection methods …

Deep learning-based object detection solutions emerged from computer vision has
captivated full attention in recent years. The growing UAV market trends and interest in …