Two-Stage Image Classifier for Autonomous Vehicles
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Solution Overview
Problem
Existing image classification techniques for autonomous vehicles are deficient in accurately classifying objects in images captured under varying conditions, such as blurry images due to high-speed travel, leading to inaccurate object detection and compensation.
Innovation Solution
Implementing a two-stage image classifier that is trained on varying capture parameters and image calibration parameters, where the second classifier is selected based on the output of the first classifier to improve classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single-stage image classifier is used for object detection, then the system complexity is low, but the classification accuracy deteriorates under varying capture conditions
Solution Approach 1:
The image classification process is divided into two distinct stages: a first classifier that performs initial classification, and a second classifier that performs refined classification. This segmentation allows each classifier to specialize in specific tasks, with the first classifier handling general categorization and the second classifier providing precise classification for specific object types, thereby improving overall accuracy without requiring a single overly complex model
Solution Approach 2:
The system dynamically selects which classifier to apply based on the output of the first classifier. When the first classifier identifies certain object types or conditions, the system adaptively activates the second classifier for those specific cases. This dynamic approach allows the system to maintain low complexity for most images while achieving high accuracy when needed, resolving the contradiction between simplicity and precision
2Adaptability or versatility
If the classifier is trained on diverse capture parameters, then the adaptability to varying conditions improves, but the training data requirements and processing complexity increase
Solution Approach 1:
Instead of training a single classifier to handle all possible capture conditions uniformly, the system trains different classifiers for different local conditions. The first classifier is trained on general features, while the second classifier is trained specifically on objects or conditions where refined classification is needed. This local specialization allows each classifier to be optimized for its specific domain, improving adaptability without requiring one excessively complex model to handle all scenarios
Solution Approach 2:
The first classifier performs preliminary classification to identify which images or object types require further analysis. By pre-filtering the data stream, the system determines in advance which cases need the more resource-intensive second classifier. This preliminary action reduces the overall processing burden and training complexity, as the second classifier only needs to handle specific subsets of data rather than all possible inputs
Data Source
AI summary
Aspects of the subject technology relate to systems, methods, and computer-readable media for image classification through a two-stage classifier. Raw image data of an image gathered by a sensor associated with an AV during operation of the AV is accessed. A first stage of a two-stage classifier is applied. The first stage is trained by first raw AV data captured at varying values of one or more capture parameters associated with one or more sensors of the AV in capturing the first raw AV data. A second stage of the two-stage classifier is applied to the raw image data to generate a final classification output. The second stage of the two-stage classifier if formed by a plurality of image calibration classifiers that are trained by second raw AV data at varying values of one or more image calibration parameters.