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Image Annotation

for A US-based smart waste solution company

0 K
Annotation / Day
0 %
Accuracy Level

About the
Customer

This Atlanta based Waste Management company was the first Unicorn in Trash. Hailed as the Uber for Trash, this fast-paced start-up’s mission is to end waste. They offer a suite of SaaS products for waste, recycling, and smart city solutions and uses technology to drive environmental innovation by helping conglomerates and governments to manage waste and develop sustainable recycling practices, as well as turn neighbourhoods into cleaner and smarter places to live and work.

Problem

Waste management companies are dependent on the quality and efficiency with which the bales, containers and materials are sorted. By digitalizing their waste collection methods, this company aimed to improve the identification of waste and waste containers, a crucial process in managing waste and recycling services. With Sorting being a major bottleneck, they began looking up at Artificial intelligence and other Machine Learning technologies to assist them in identifying and organising waste.

Solution

NextWealth partnered with the client to use their expertise to identify and label waste and waste containers, based on computer vision that annotates images, video, or real-time video capture. Image Recognition and Image Annotation techniques allowed granular identification of pieces of waste and helped tag data for their Machine Learning (ML) models. The tags were analysed and categorised to identify appropriate disposal or recycle mechanisms, following which safe disposal or clean recycling was carried out.

Results

Accurate annotation ensured that waste materials were identified and labelled properly, thus helping the company build training data to train their machine learning models. By combining computer vision, machine learning, and artificial intelligence, the company was able to synchronize sorting and picking up recycled materials.

Services Provided

Bounding Box Annotation

Image Annotation

Data Labelling

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