#learnopencv

[mft-toyama]

🔥YOLOv8 is finally here! Check out our video, which shows off the object detection and instance segmentation prediction results using the Ultralytics YOLOv8x model. ➡️ https://t.co/OHjVyBbUpO #yolo #yolov8 #yolov5 #objectdetection #deeplearning #ai #computervision https://t.co/mjoXrJrbx3

— Satya Mallick (@LearnOpenCV) Jan 11, 2023

[eurekakinginc]

Posted by spmallick via /r/artificial. Join Discussion: https://ift.tt/2GoNPf9. Curated by: www.eurekaking.com

[aioftheday]

Latest #AI #Business #Policies #Government https://t.co/dNQ4ugt9kH Thanks to @jlmico @FACEmeeting @LearnOpenCV #machinelearning

Latest #AI #Business #Policies #Government https://t.co/dNQ4ugt9kH Thanks to @jlmico @FACEmeeting @LearnOpenCV #machinelearning

— AI Of The Day (@AIofTheDay) March 1, 2017

from Twitter https://twitter.com/AIofTheDay March 01, 2017 at 01:34PM via IFTTT

[learnopencv1]

YOLOv7 is a single-stage real-time object detector. It was introduced to the YOLO family in July’22. According to the YOLOv7 paper, it is the fastest and most accurate real-time object detector to date. YOLOv7 established a significant benchmark by taking its performance up a notch.

[learnopencv1]

Since its inception, the YOLO family of object detection models has come a long way. YOLOv7 is the most recent addition to this famous anchor-based single-shot family of object detectors. It comes with a bunch of improvements which include state-of-the-art accuracy and speed.  In this article, we will be fine tuning the YOLOv7 object detection model on a real-world pothole detection dataset.

[learnopencv1]

Arguably, the most crucial task of Deep Learning-based Multiple Object Tracking (MOT) is not to identify an object but to re-identify it after occlusion. There are a plethora of trackers available to use, but not all of them have a good re-identification pipeline. In this blog post, we will focus on one such tracker, FairMOT, that revolutionized the joint optimization of detection and re-identification tasks in tracking.

[learnopencv1]

Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. In this article, we will create a drowsy driver detection system to address such an issue. For this, we will use Mediapipe’s Face Mesh solution in python and the Eye Aspect ratio formula. Our goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time.

In this post, we will:

Learn how to detect eye landmarks using the Mediapipe Face Mesh solution pipeline in python.

Introduce and demonstrate the Eye Aspect Ratio (EAR) technique. 

Create a Driver Drowsiness Detection web application using streamlit.

Use streamlit-webrtc to help transmit real-time video/audio streams over the network. 

Deploy it on a cloud service.

[learnopencv1]

Driver drowsiness detection system alerts the driver if they feel drowsy or fall asleep behind the wheel. Continuous driving can be tedious and exhausting. A motorist may get droopy and perhaps nod off due to inactivity. We will use Mediapipe’s Face Mesh solution in python and the Eye Aspect ratio formula. Our goal is to create a robust and easy-to-use application that detects and alerts users if their eyes are closed for a long time. We will discuss the following: ✅Learn how to detect eye landmarks using the Mediapipe Face Mesh solution pipeline in python. ✅Introduce and demonstrate the Eye Aspect Ratio (EAR) technique. ✅Create a Driver Drowsiness Detection web application using streamlit. ✅Use streamlit-webrtc to help transmit real-time video/audio streams over the network. ✅Deploy it on a cloud service. ❓FAQ How does driver drowsiness detection work? Which algorithm is used for driver drowsiness detection? How can we detect when a driver is falling asleep?

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[aioftheday]

Latest #AI #Business #Policies #Government https://t.co/dNQ4ugt9kH Thanks to @LearnOpenCV @QueenRex #ai #machinelearning

Latest #AI #Business #Policies #Government https://t.co/dNQ4ugt9kH Thanks to @LearnOpenCV @QueenRex #ai #machinelearning

— AI Of The Day (@AIofTheDay) February 27, 2017

from Twitter https://twitter.com/AIofTheDay February 27, 2017 at 09:33AM via IFTTT