How to detect vehicle License Plate? Contents: Load the pre-trained model,
Image pre-processing, Get the license plate.

The article explains what object detection is, overviews its use cases across industries, and highlights a case study for an object detection system development

Object detection is a product of Computer Vision and is a very effective technique to precisely locate items of different shapes and sizes and label them.

Part of the broader artificial intelligence and computer vision realms, human pose estimation (HPE) technology has been gradually making its presence seen in all kinds of software apps and hardware solutions. Still, human pose estimation seemed to be stuck…

In this guide, we'll go over everything you need to know about Automatic Number Plate Recognition (ANPR) solutions, such as how they work, how they're used etc

Sound has been used in numerous applications as a tool for object localization. Audio technology can enhance object detection and may improve self driving cars.

The function of the program is to start an infinite loop that reads a certain area of the screen where the poker table is.

You will find out how we at inDrive currently handle regular vehicle verifications. Every month, we ask our users to take a picture of their car.

Frameworks for object detection and computer vision tasks are indeed numerous. This article attempts to highlight the available frameworks for object detection.

A story of one image recognition project. From optimizing an object detection model and optimizing a dataset to multistage neural networks.

A dive into developing an image recognition app without using neural networks

Explore the cutting-edge integration of YOLOv3 and PolyRNN++ for automated image analysis.

In this article, I’ll outline how we used Amazon Rekognition to rapidly build an image object detection feature for a social media startup.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

How to build a system that automatically detects and categorizes objects in media as soon as users upload it, storing the results in a database for future use.