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Object Detection in 2024: The Definitive Guide

Viso.ai

This article will provide an introduction to object detection and provide an overview of the state-of-the-art computer vision object detection algorithms. The recent deep learning algorithms provide robust person detection results. Detecting people in video streams is an important task in modern video surveillance systems.

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Faster R-CNNs

PyImageSearch

One of the most popular deep learning-based object detection algorithms is the family of R-CNN algorithms, originally introduced by Girshick et al. Since then, the R-CNN algorithm has gone through numerous iterations, improving the algorithm with each new publication and outperforming traditional object detection algorithms (e.g.,

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The Complete Guide to OpenPose in 2025

Viso.ai

However, in recent years, human pose estimation accuracy achieved great breakthroughs with Convolutional Neural Networks (CNNs). The method won the COCO 2016 Keypoints Challenge and is popular for quality and robustness in multi-person settings. The object detection algorithm can determine the region of individuals.

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4 Applications of Intelligent Waste Management [2025]

Viso.ai

billion tons of municipal solid waste was generated globally in 2016 with experts predicting a steep rise to 3.40 For truly solving real-world scenarios, organizations require more than just a computer vision tool or algorithm. These systems coordinate sensors and visual algorithms to monitor garbage levels.

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YOLOv7: The Most Powerful Object Detection Algorithm (2023 Guide)

Viso.ai

The YOLOv7 algorithm is making big waves in the computer vision and machine learning communities. In this article, we will provide the basics of how YOLOv7 works and what makes it the best object detector algorithm available today. The original YOLO object detector was first released in 2016. higher AP (average precision).

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Computer Vision in Autonomous Vehicle Systems

Viso.ai

After that, they utilize specialized algorithms to identify trends, predict outcomes, and absorb fresh data. 2016) introduced a unified framework to detect both cyclists and pedestrians from images. It is achieved by computer vision algorithms. The eyes of the automobile are computer vision models.

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Multi-Modal Methods: Image Captioning (From Translation to Attention)

ML Review

These new approaches generally; Feed the image into a Convolutional Neural Network (CNN) for encoding, and run this encoding into a decoder Recurrent Neural Network (RNN) to generate an output sentence. Finally, one can use a sentence similarity evaluation metric to evaluate the algorithm.