YOLOv13 seamlessly combines hypergraph computation with end-to-end information collaboration to deliver a more accurate, robust, and efficient real-time detection solution.

Recently, mmWave radars have been used in several automotive and industrial applications to provide the position and speed of detected objects using FMCW algorithms. However, this radar is not able to ...

This realization has far-reaching consequences: With the help of intelligent algorithms, optical measurement methods could be significantly improved in a wide range of areas—from medical ...

Introduced in the paper "Roboflow 100-VL: A Multi-Domain Object Detection Benchmark for Vision-Language Models", RF100-VL is a large-scale collection of 100 multi-modal datasets with diverse concepts ...

A new technical paper titled “Road Boundary Detection Using 4D mmWave Radar for Autonomous Driving” was published by Stanford University. Abstract “Detecting road boundaries, the static physical edges ...

To further evaluate its effectiveness, we compared LFN-YOLO with other leading object detection algorithms using the TrashCan dataset (Hong et al., 2020). This dataset comprises 7,212 underwater ...

Object detection is an essential step in various applications. After deep learning appeared, convolutional neural networks or transformers have shown significant improvement in object detection ...

The object detection method serves as the core technology within the unmanned driving perception module, extensively employed for detecting vehicles, pedestrians, traffic signs, and various objects.