A YOLOv5-LiDAR basketball-robot system achieved 97.85% detection accuracy and high task-success rates in controlled dataset evaluations and simulations, with extreme motion blur and dense occlusion ...

YOLO, a lightweight and explainable AI framework that detects wildlife from camera-trap images faster and more efficiently ...

An AI Wildlife-Crossing Alert System for Forest Roads Using Arduino UNO Q is a solar-powered edge-AI safety system designed ...

Ultralytics is the company behind YOLO, an open-source computer vision model used for tasks including detection, segmentation ...

This project implements a real-time computer vision pipeline using YOLO object detection, object tracking, centroid calculation, bounding-box size estimation, FPS measurement, and inference latency ...

What if a device could see the world the same way humans do, seeing objects, recognizing them, and understanding what they are in real time? Just like our eyes capture visuals and our brain instantly ...

Imagine a vehicle cruising at 54 kilometres per hour, roughly the speed of an object moving 0.5 metres per frame at 30 frames per second (0.5×30×3.6=54 km/hr). Mounted on the vehicle’s roof is a ...

Underwater object detection plays a significant role in fisheries resource assessment and ecological environment protection. However, traditional underwater object detection methods struggle to ...

The popular Ultralytics YOLO11 AI model was compromised in a supply chain attack to deploy cryptominers on devices running versions 8.3.41 and 8.3.42 from the Python Package Index (PyPI) Ultralytics ...

Abstract: In the present research work, a system is developed that can detect objects in real-time using a combination of the ESP32 CAM module and Python programming. The goal was to show how ...