Creating Effective Computer Vision Models for Car Position Detection Using Amazon SageMaker and Amazon Rekognition
Introduction:
Computer vision (CV) is a widely used application of machine learning (ML) and deep learning, with applications ranging from self-driving cars to cancer detection. Amazon Rekognition is a managed service that can perform CV tasks such as object detection and content moderation without prior ML experience. In this post, we explore how CV can be applied to customer-facing mobile applications that require image upload, like online shopping platforms. We discuss two options for detecting the position of a car in an image: using Amazon Rekognition’s rule-based system or training a custom ML model with Amazon SageMaker. We also provide an overview of the solution architecture and demonstrate how to integrate the car pose detection solution into existing web applications using services like Amazon API Gateway and AWS Amplify.
Full Article: Creating Effective Computer Vision Models for Car Position Detection Using Amazon SageMaker and Amazon Rekognition
How Computer Vision Can Enhance Car Pose Detection
Computer vision (CV), powered by machine learning (ML) and deep learning, is widely used in various applications such as self-driving cars, social media content moderation, cancer detection, and defect detection. One of the key areas where CV can be applied is in customer-facing mobile applications that require image uploads. In this article, we will explore how CV can be utilized to detect the position and pose of objects, specifically focusing on cars.
Using Amazon Rekognition for Car Pose Detection
Amazon Rekognition is a fully managed service that allows users to perform CV tasks without the need for prior ML experience. It offers capabilities such as object detection, video segment detection, and content moderation. For car pose detection, one option is to use Amazon Rekognition in combination with a rule-based system.
In this approach, the wheels of the car are detected using Amazon Rekognition. Then, a rule-based system is used to infer the car’s orientation based on the positions of the wheels. This solution does not require ML expertise and can be trained on publicly available datasets. Additionally, it can be integrated into existing web applications using services like Amazon API Gateway and AWS Amplify.
Improving Wheel Detection with Amazon Rekognition Custom Labels
To enhance the accuracy of wheel detection, Amazon Rekognition Custom Labels can be utilized. This feature allows users to train a custom image analysis model using their own labeled data. By fine-tuning the model with car images of specific angles, the detection accuracy can be improved. Amazon Rekognition Custom Labels seamlessly integrates with the standard Amazon Rekognition solution and requires only a few adaptations to the Lambda function.
Training a Custom ML Model using Amazon SageMaker
Another option for car pose detection is to train a custom deep learning model using Amazon SageMaker. The Detectron2 framework, which provides state-of-the-art detection and segmentation algorithms, is used for this purpose. Transfer learning is applied to a pretrained Mask R-CNN model, which is then fine-tuned on a car parts segmentation dataset.
The dataset consists of annotated images of sedans, pickups, and SUVs, with annotations for different car parts such as wheels, mirrors, lights, and glass. By training the model on this dataset, it becomes capable of detecting wheels and other car parts, which can further improve the car angle computation. The trained model and dataset are stored in Amazon Simple Storage Service (Amazon S3), while the Docker container required for the Lambda function is stored in Amazon Elastic Container Registry (Amazon ECR).
Integrating Car Pose Detection into Web Applications
To integrate the car pose detection solution into existing web applications, a mock web application is deployed using AWS Amplify. Users can upload an image, which is then processed by either the Amazon Rekognition model or the custom Detectron model. AWS Lambda functions are utilized behind an API Gateway to enable this functionality.
Conclusion
Computer vision, powered by machine learning and deep learning, offers valuable solutions in various domains, including car pose detection. With options like Amazon Rekognition and Amazon SageMaker, developers can easily implement car pose detection in their applications. Whether using a rule-based system or training a custom ML model, these technologies provide efficient and accurate results that can enhance user experience and engagement.
Summary: Creating Effective Computer Vision Models for Car Position Detection Using Amazon SageMaker and Amazon Rekognition
Computer vision (CV) is a widely used application of machine learning (ML) and deep learning. It has various applications such as self-driving cars, content moderation on social media, cancer detection, and defect detection. Amazon Rekognition is a fully managed service that performs CV tasks like object detection, content moderation, and more without prior ML experience. This article explores the use of CV in customer-facing mobile applications, where image upload is required. It demonstrates how to combine ML solutions with post-processing to detect the pose of objects using AWS Cloud. The article provides two options, one using Amazon Rekognition and another using Amazon SageMaker, and explains how to integrate the solution into a web application. It also discusses using Amazon Rekognition Custom Labels to improve wheel detection and training a custom model using SageMaker.
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