Automatic Ground Truth Generation for ML Object Detection
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Solution Overview
Problem
Conventional object detection systems are limited in their ability to accurately detect objects from different perspectives, requiring extensive manual labeling and retraining of machine learning models, which is time-consuming and resource-intensive, and are not easily scalable.
Innovation Solution
The system automatically generates ground truth data by leveraging outputs from existing machine learning models to train or retrain models for accurate detection and identification of objects from various perspectives, using object tracking algorithms to fill detection gaps without manual labeling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling is used to generate ground truth data for each new perspective, then detection accuracy from that perspective is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs preliminary object detection on a subset of images from the new perspective to identify objects and their locations. This preliminary action creates initial ground truth data that can be propagated to other images through object tracking, avoiding the need for manual labeling of every image.
Solution Approach 2:
The system copies object detection results from images where objects are detected to corresponding images where they are not detected, using object tracking algorithms. This copying mechanism transfers ground truth information across the image sequence, eliminating the need for repetitive manual labeling.
2Reliability
If manual labeling is performed for all images to ensure accurate training data, then model training accuracy is improved, but computational resources and human effort increase
Solution Approach 1:
The system performs manual or automated labeling only on a partial subset of images rather than all images. Object tracking algorithms then extend this partial labeling coverage to the complete image sequence, achieving sufficient training data coverage without exhaustive labeling effort.
Solution Approach 2:
The system uses its own object detection capabilities to generate ground truth data automatically. The machine learning model detects objects in some images, and this detection information serves as self-generated ground truth that propagates through object tracking to create training data for the entire dataset.
3Measurement precision
If the machine learning model is trained on image data from a specific perspective, then detection accuracy from that perspective is improved, but the model becomes ineffective when applied to images from different perspectives
Solution Approach 1:
The system trains the machine learning model on ground truth data generated from multiple perspectives through object tracking. By propagating object information across images captured from different angles and positions, the model learns perspective-invariant object representations, enabling it to function effectively across diverse viewing conditions.
Data Source
AI summary
In various examples, object detections of a machine learning model are leveraged to automatically generate new ground truth data for images captured at different perspectives. The machine learning model may generate a prediction of a detected object at the different perspective, and an object tracking algorithm may be used to track the object through other images in a sequence of images where the machine learning model may not have detected the object. New ground truth data may be generated as a result of the object tracking algorithms outputs, and the new ground truth data may be used to retrain or update the machine learning model, train a different machine learning model, or increase the robustness of a ground truth data set that may be used for training machine learning models from various perspectives.