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How Quinnox’s Automated Dumpster Image Classification with Deep Learning Helped a Leading Environmental Services Company Improve Operational Efficiency

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Overview

The client is a leading environmental service provider who captures dumpster images using smart truck cameras or manually by the driver. These images are sent to a third-party application for manual classification as overloaded or not overloaded, based on the amount of waste. This classification process involves a two-level manual audit that is time-consuming, prone to errors, and impacts operational efficiency. 

Business Need

  • The client required a streamlined and automated solution to: 

    • Improve the accuracy of waste classification. 
    • Eliminate errors caused by the current manual audit process. 
    • Address issues of incorrect invoicing to reduce revenue leakage. 
    • Enhance customer satisfaction by ensuring precise and timely classification. 

The Quinnox Solution

  • Developed a deep learning solution to optimize the client’s manual audit process using AI-based image recognition techniques. 
  • Automated the first-level auditor’s role in classifying dumpster images as overloaded or not overloaded. 
  • The model was trained on thousands of images to achieve over 90% accuracy in classification. 

Tech Used: Keras for deep learning, Open CV for image processing & Python 

Business Benefits Delivered

  1. Improved trust and efficiency for call agents, resulting in enhanced customer service. 
  2. Achieved indirect revenue gains through better call agent efficiency. 
  3. Ensured higher accuracy and availability of data for proof of service or no service. 

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