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 ...

Build practical Edge AI applications with Raspberry Pi, from basic concepts to object detection and robotics, using the AI HAT+ and Camera Module 3. Edge AI is bringing data processing directly onto ...

YOLO object detection service for MQTT camera events — subscribes to motion event images from camera bridges, runs inference via YOLO26, and publishes detection results back to MQTT. Designed to work ...

What if your Raspberry Pi could do more than just compute, it could see the world like you do? Imagine a tiny device that doesn’t just identify a dog in a photo but tells you whether it’s lounging on ...

Urban infrastructure element detection is important for the domain of public management in large urban centres. The diversity of objects in the urban environment makes object detection and ...

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 ...

You Only Look Once (YOLO) is a CNN architecture for performing real-time object detection. The algorithm applies a single neural network to the full image, and then divides the image into regions and ...

What if you could teach a computer to recognize a zebra without ever showing it one? Imagine a world where object detection isn’t bound by the limits of endless training data or high-powered hardware.

Abstract: Accurate and stable target detection is crucial for robotic grasping tasks under uneven lighting conditions. To address this, this paper proposes a target object detection network (YOLO-Net) ...

We present a new dataset combined with the DeepSee model, which utilizes the YOLOv8 architecture, designed to rapidly and accurately detect benthic lifeforms in deep-sea environments of the North ...