Abstract: Object detection is a fundamental procedure in the interpretation of remote sensing images. In large-scale remote sensing images, it is common to observe that the interesting objects only ...

This project implements a comprehensive Computer Vision MLOps pipeline for aerial object analysis, specifically designed to classify and detect birds vs drones in aerial imagery. The system combines: ...

With the rapid development of marine resource exploitation and the increasing demand for underwater robot inspection, achieving reliable target perception in turbid, low-illumination, and spectrally ...

ABSTRACT: The study adapts several machine-learning and deep-learning architectures to recognize 63 traditional instruments in weakly labelled, polyphonic audio synthesized from the proprietary Sound ...

Traffic monitoring plays a vital role in smart city infrastructure, road safety, and urban planning. Traditional detection systems, including earlier deep learning models, often struggle with ...

A new study led by researchers from the Yunnan Observatories of the Chinese Academy of Sciences has developed a neural network-based method for large-scale celestial object classification, according ...

Abstract: Few-shot object detection (FSOD) addresses the challenge of limited labeled data by enabling detectors to learn from minimal annotations. Recent work on image–text fusion has shown promise ...

Small object detection is a critical task in applications like autonomous driving and ship black smoke detection. While Deformable DETR has advanced small object detection, it faces limitations due to ...

TOKYO, May 19, 2025 /PRNewswire/ -- Synspective Inc., a provider of Synthetic Aperture Radar (SAR) satellite data and analytics solutions, is pleased to announce the launch of its Object Detection and ...