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Case Study
Data ScienceArtificial Intelligence - Powered Tyre Dimension Extraction System
Overview
JashDS developed an AI-powered computer vision system for a leading automotive e-commerce platform, enabling accurate extraction of tire dimensions from images. The solution, which increased conversion rates by 25% and reduced customer support inquiries by 80%, utilized advanced technologies such as YoloV8 for instance segmentation and custom-designed augmentation techniques to simplify the online tire purchasing process.
About the Client
The e-commerce platform of one of the world’s largest tire manufacturers.
The Challenge
The client faced difficulties in assisting online customers who wanted to purchase new tires but were unsure of their vehicle's tire dimensions. This uncertainty led to reduced conversion rates and potential customer dissatisfaction. The challenge was to develop an automated system that could accurately extract tire dimensions from images, simplifying the purchasing process for customers and increasing sales for the e-retailer.
Key Results
- Increased e-commerce conversion rates by 25% for tire purchases.
- Reduced customer support inquiries related to tire sizing by 80%.
- Improved customer satisfaction scores significantly for the tire purchasing process.
Our Solution
JashDS developed a sophisticated computer vision system to extract tire dimensions from images:
- Implemented an instance segmentation model (YoloV8) for zone detection, identifying areas containing tire dimensions, model, brand, and DOT information.
- Developed a cropping and orientation correction algorithm to prepare detected zones for further processing.
- Utilized an object detection model for character recognition within the cropped images.
- Created a post-processing system with pattern checks to verify the accuracy of extracted tire dimensions.
- Designed custom augmentation techniques based on model explanations to improve detection accuracy.
- Implemented parallelization of zone processing to enhance system efficiency.
- Developed a zone correction mechanism using pattern matching to further improve accuracy.
- Utilized Azure ML pipeline to track experiment results and manage the machine learning workflow.
Technologies Used
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