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NOVEMBER 5, 2024

The YOLO Family of Models The first YOLO model was introduced back in 2016 by a team of researchers, marking a significant advancement in object detection technology. Convolution Layer: The concatenated feature descriptor is then passed through a Convolution Neural Network.

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DECEMBER 3, 2023

Hence, rapid development in deep convolutional neural networks (CNN) and GPU’s enhanced computing power are the main drivers behind the great advancement of computer vision based object detection. Various two-stage detectors include region convolutional neural network (RCNN), with evolutions Faster R-CNN or Mask R-CNN.

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OCTOBER 7, 2024

billion tons of municipal solid waste was generated globally in 2016 with experts predicting a steep rise to 3.40 Object Detection : Computer vision algorithms, such as convolutional neural networks (CNNs), analyze the images to identify and classify waste types (i.e., As per the World Bank, 2.01 billion tons in 2050.

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DECEMBER 6, 2024

Advanced driver assistance systems (ADAS) and automated driving systems (ADS) are both new forms of driving automation. Levels of Automation in Vehicles – Source Here we present the development timeline of the autonomous vehicles. 2016) introduced a unified framework to detect both cyclists and pedestrians from images.

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MARCH 11, 2024

YOLO in 2015 became the first significant model capable of object detection with a single pass of the network. The previous approaches relied on Region-based Convolutional Neural Network (RCNN) and sliding window techniques. What is YOLO?

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MAY 1, 2024

Deep learning and Convolutional Neural Networks (CNNs) have enabled speech understanding and computer vision on our phones, cars, and homes. Moley Robotic Kitchen with 2 arms – Source The Moley kitchen is an automated kitchen unit, consisting of cabinets, and robotic arms. Stone and R. Brooks et al. Brooks et al.

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OCTOBER 15, 2024

In the field of real-time object identification, YOLOv11 architecture is an advancement over its predecessor, the Region-based Convolutional Neural Network (R-CNN). Using an entire image as input, this single-pass approach with a single neural network predicts bounding boxes and class probabilities. Redmon, et al.

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