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Sustainability

Teknoir’s AI Sustainability Detection App is a versatile solution for companies aiming to achieve their ESG (Environmental, Social, and Governance) objectives. It harnesses advanced computer vision techniques, including our suite of deep learning convolutional neural networks (CNNs) and vision language models (VLMs). These models are designed to support a variety of sustainability use cases. What's more, our AI apps are easily deployed on lightweight, wireless computers at the edge, in the cloud, or hybrid cloud-edge, adapting to the unique architecture and computing requirements of each customer.

The Experience: Teknoir Recyclable Object Detection

 

Teknoir is dedicated to addressing complex operational challenges encountered by businesses striving to meet their Environmental, Social, and Corporate Governance (ESG) objectives.

Our ESG-focused AI vision solution deploys cameras at the edge to autonomously categorize waste and recyclable byproducts while assessing their characteristics, such as material type, size, and quantity. This data equips our clients to make data-informed choices regarding efficient recycling and waste management techniques and to monitor ESG-related Key Performance Indicators (KPIs).

Through the Teknoir platform, businesses can optimize recycling procedures, curtail disposal expenditures, capitalize on fresh revenue streams by reclaiming materials, and fortify their standing as conscientious environmental custodians.

The Science: Teknoir Recyclable Material Classification

 

Teknoir’s AI computer vision models can detect and classify recyclable materials in complex settings, such as landfills, processing plant conveyors, etc.

Our AI solutions for waste and recycling help organizations minimize downtime by detecting non-recyclable goods that can damage equipment. We also enable our customers to quantify the impact of their recycling programs for reporting and compliance purposes.

AI Models

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Recyclable Object Detector

Our recyclable object detection model has been pre-trained on thousands of images and utilizes deep learning algorithms for identifying waste and recyclable objects. These models are able to spatially identify objects in 2D and 3D space. The outputs of these detections can be configured as real time alerts, KPI dashboards, summarized reports, or sent to downstream processing equipment.
Waste & Recycling

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Semantic Segmentation & Object Recognition

Deep learning techniques like deep convolutional neural networks allow us to perform semantic segmentation, which is integral to understanding the context within which objects exist in images. This is crucial for precise identification of materials or anomalies. Object detection algorithms that include segmentation provide detailed insights by not only locating the bounding box around an object but also delineating the exact shape of the object. While Object Detection involves locating objects within an image, typically using bounding boxes, recognition delves deeper, aiming to identify the specific nature or class of the detected objects. This distinction is important in tailoring computer vision systems for specific applications. For instance, while a detection system might be sufficient for counting recyclables on a conveyor belt, a recognition system would be necessary to differentiate between recyclable materials such as plastic, paper, cardboard, aluminum, and Styrofoam.
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