Neural Network Training for Temporally Stable Semantic Segmentation
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Solution Overview
Problem
Deep neural networks (DNNs) used in automated driving systems are not robust to variations in the input domain, leading to unstable predictions over time, which is critical for safety, and existing methods like adversarial training and label propagation increase training time and cost without significantly improving temporal stability.
Innovation Solution
A method that combines frame-based and sequence-based evaluations for semantic image segmentation, using cross-entropy loss for individual frames and temporal consistency loss for consecutive frames, optimized by a weighted sum of these losses, to enhance stability and accuracy in neural network training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adversarial training or label propagation techniques are used to improve temporal stability, then prediction stability may be improved, but training time increases significantly
Solution Approach 1:
The patent segments the training evaluation into two distinct components: frame-based evaluation (assessing individual frame predictions) and sequence-based evaluation (assessing temporal consistency across frames). This segmentation allows the system to identify specific stability issues without requiring extensive additional training data, thereby improving temporal stability assessment efficiency.
Solution Approach 2:
The patent implements feedback mechanisms where evaluation results from both frame-based and sequence-based assessments are fed back into the training process. This feedback loop enables the system to iteratively improve temporal stability by adjusting training based on identified weaknesses, reducing the need for time-consuming adversarial training approaches.
2Reliability
If adversarial training or label propagation techniques are used to improve temporal stability, then prediction stability may be improved, but additional costs due to labelling and increased training resources are incurred
Solution Approach 1:
The patent enables the training system to self-evaluate its temporal stability performance through automated frame-based and sequence-based evaluation. This self-service approach eliminates the need for manual labelling of additional images for stability assessment, significantly reducing training costs and complexity while maintaining improved temporal stability.
Solution Approach 2:
By implementing feedback loops that automatically evaluate and adjust training based on temporal stability metrics, the system reduces dependency on expensive manual labelling processes. The feedback mechanism guides training improvements efficiently, lowering overall training costs.
3Measurement precision
If DNNs are trained on varied datasets to ensure generalization, then accuracy on different domains is improved, but robustness to domain variations deteriorates
Solution Approach 1:
The patent applies local quality by evaluating temporal consistency specifically for sequence-based assessments rather than treating all predictions uniformly. This localized evaluation approach allows the system to identify and improve robustness to domain variations in temporal sequences while maintaining high segmentation accuracy across different domains.
Solution Approach 2:
Instead of only evaluating individual frame accuracy, the patent inverts the approach by evaluating temporal consistency across sequences. This inversion reveals domain variation robustness issues that single-frame evaluation misses, enabling improved generalization to varied domains while maintaining accuracy.
Data Source
AI summary
The present invention relates to a method, a computer program, and an apparatus for training a neural network for semantic image segmentation. The invention further relates to an in-car control unit or a backend system, which make use of such a method or apparatus, and to a vehicle comprising such an in-car control unit. In some embodiments and in a first step, image data of a sequence of image frames are received. Then a frame-based evaluation of semantic segmentation predictions of one or more objects in individual image frames is performed. Furthermore, a sequence-based evaluation of temporal characteristics of semantic segmentation predictions of said one or more objects in at least two image frames is performed. The results of the frame-based evaluation and the sequence-based evaluation are combined.