This article presents a comprehensive survey of deep learning applications for object
detection and scene perception in autonomous vehicles. Unlike existing review papers, we …

Over the last years deep learning methods have been shown to outperform previous state‐of‐
the‐art machine learning techniques in several fields, with computer vision being one of the …

Unsupervised domain adaptive object detection aims to adapt detectors from a labelled
source domain to an unlabelled target domain. Most existing works take a two-stage strategy …

Pedestrian detection is an important but challenging problem in computer vision, especially
in human-centric tasks. Over the past decade, significant improvement has been witnessed …

Though unsupervised domain adaptation (UDA) has achieved very impressive progress
recently, it remains a great challenge due to missing target annotations and the rich …

Convolutional neural networks (CNNs) have found applications in ship detection from
synthetic aperture radar (SAR) images. However, there are some challenges hamper their …

Distance estimation and pedestrian detection are critical for safe driving operation decision-
making and autonomous vehicle intelligent control strategies. This paper proposes a novel …

Domain adaptation aims to bridge the domain shifts between the source and the target
domain. These shifts may span different dimensions such as fog, rainfall, etc. However …

Pedestrian detection has emerged as a fundamental technology for autonomous cars,
robotics, pedestrian search, and other applications. Although many excellent object …

Traditional smoke recognition methods are mainly based on handcrafted features. However,
it is difficult to design handcrafted features that are robust and discriminative for smoke …