In computer vision, 2D convolution is arguably the most important operation performed by a
ConvNet. Unsurprisingly, it has been the focus of intense software and hardware …

Monocular 3D lane detection is a challenging task due to its lack of depth information. A
popular solution is to first transform the front-viewed (FV) images or features into the bird-eye …

Since the 2004 DARPA Grand Challenge, the autonomous driving technology has
witnessed nearly two decades of rapid development. Particularly, in recent years, with the …

Autonomous railway inspection with unmanned aerial vehicles (UAVs) has huge
advantages over traditional inspection methods. As a prerequisite for UAV‐based …

Existing IoU-based loss functions have achieved promising performance for bounding box
regression in object detection. However, they cannot fully reflect the relation between the …

3D lane detection plays a crucial role in autonomous driving by extracting structural and
traffic information from the road in 3D space to assist the self-driving car in rational, safe, and …

Abstract 3D lane detection from monocular images is a fundamental yet challenging task in
autonomous driving. Recent advances primarily rely on structural 3D surrogates (eg, bird's …

Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of
large foundation models in novel scenarios with limited training data. In the computer vision …

Although previous works on traffic scene understanding have achieved great success, most
of them stop at a lowlevel perception stage, such as road segmentation and lane detection …

3D lane detection is an integral part of au-tonomous driving systems. Previous CNN and
Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from …