With the development of convolutional neural networks, hundreds of deep learning–based
dehazing methods have been proposed. In this article, we provide a comprehensive survey …

Typical representations for arbitrary-oriented object detection tasks include the oriented
bounding box (OBB), the quadrilateral bounding box (QBB), and the point set (PointSet) …

Source-free object detection (SFOD) aims to transfer a detector pre-trained on a label-rich
source domain to an unlabeled target domain without seeing source data. While most …

Remote sensing images (RSIs) contain important information, such as airports, ports, and
ships. By extracting RSI features and learning the mapping relationship between image …

DETR-style detectors stand out amongst in-domain scenarios, but their properties in domain
shift settings are under-explored. This paper aims to build a simple but effective baseline …

Unmanned aerial vehicles (UAVs) have been widely adopted to assist infrastructure
inspection tasks, since they have shown their potential to benefit the inspection in terms of …

Capturing images is a key part of automation for high-level tasks such as scene text
recognition. Low-light conditions pose a challenge for high-level perception stacks, which …

Social media platforms have emerged as vital sources of sharing real-time information
during crises, enabling users to share critical details about disasters and ensuring timely …

Abstract Domain Adaptive Object Detection (DAOD) alleviates the reliance on labeled data
by transferring knowledge learned from labeled source domain to unlabeled target domain …

Following the successful contribution of machine vision systems to automated inspection
and quality control, in this paper, we propose a new bounding boxes-based regression …