What if technology could bridge the gap between spoken language and sign language, empowering millions of people to communicate more seamlessly? With advancements in deep learning, this vision is no ...

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 ...

Often, organizations face challenges in deploying mission-critical systems, such as those for national security, that rely on Machine Learning (ML) capabilities. One of those challenges comes from ML ...

Abstract: With the rapid development of UAV (Unmanned Aerial Vehicle) and infrared imaging technology, object detection based on the resultant images has demonstrated its potential application value ...

Maritime mobile edge computing (MMEC) technology facilitates the deployment of computationally intensive object detection tasks on Maritime Internet of Things (MIoT) devices with limited computing ...

Description: I have created a machine learning model for object detection using ML.net Model Builder (MLModel1.zip). The model was successfully tested using the automatically generated project by ...

Please check out our new release on YOLOE. YOLOE code: https://github.com/THU-MIG/yoloe YOLOE paper: https://arxiv.org/abs/2503.07465 Comparison of performance ...

The early diagnosis and accurate classification of lung cancer have a critical impact on clinical treatment and patient survival. The rise of artificial intelligence technology has led to ...

Roboflow has launched RF-DETR, a real-time object detection model tailored for embedded systems, edge devices, and low-latency applications. Rather than competing in the race for scale among ...

Abstract: In practical application scenarios, the objects to be detected are characterized by a large number, irregular shape, non-uniform size and dense distribution, etc. Traditional object ...