Style Transfer for Object Detection Robustness
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Solution Overview
Problem
Existing object detection models struggle to recognize images with different styles due to degradation, requiring significant manpower and resources for training on diverse image styles, and are inefficient in adapting to varied image conditions.
Innovation Solution
An image processing method that generates new style representations different from source and target domain styles, ensuring semantic consistency with source domain images, and trains an object detection model using these representations to improve adaptation and detection accuracy across multiple styles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If object detection models are trained on diverse image styles to improve detection accuracy across different conditions, then detection robustness improves, but training time and computational resources increase significantly
Solution Approach 1:
The patent pre-processes target domain images to extract style representations and generates style-transferred images before actual model training. By preparing augmented training data in advance with different styles (rain, snow, fog, night), the system reduces the need for extensive retraining when deploying to different environments, thus improving detection robustness while reducing actual training time.
Solution Approach 2:
The patent creates synthetic style-transferred images by copying and transferring style characteristics from target domain images to source domain images. This generates additional training samples with various degradation styles without requiring actual collection of diverse real-world images, thereby expanding training data diversity while avoiding the time cost of manual data collection and annotation.
2Adaptability or versatility
If object detection models are trained on degraded images with multiple styles to improve generalization, then adaptability to target domain improves, but model complexity and training difficulty increase
Solution Approach 1:
The patent separates image processing into distinct components: content representation extraction, style representation extraction, and style transfer. By dividing the complex task of handling degraded images into these modular stages, the system manages model complexity more effectively while achieving good domain generalization performance.
Solution Approach 2:
The patent introduces style representations as an intermediary between source and target domains. Instead of directly training on complex degraded images, the system uses extracted style features as a mediator to transfer domain characteristics, simplifying the adaptation process and reducing the direct impact of model complexity.
3Measurement precision
If manual annotation is performed on diverse styled images to improve training data quality, then detection accuracy improves, but annotation cost and time consumption increase
Solution Approach 1:
The patent uses style transfer to automatically generate diverse training images by copying styles from target domain images to source domain images with ground truth annotations. This approach creates multiple styled versions of annotated images without requiring manual annotation of each style variation, thereby maintaining detection accuracy while significantly reducing annotation costs.
Solution Approach 2:
The system performs automatic style transfer and data augmentation without requiring manual intervention for each styled image. The automated process generates diverse training data that maintains semantic consistency and annotation accuracy, eliminating the need for expensive manual annotation of diverse styled images.
Data Source
AI summary
The present disclosure relates to the technical field of computers, and relates to an image processing method and apparatus, and a computer readable storage medium. The method of the present disclosure comprises: obtaining a source domain content representation of a source domain image, and obtaining a target domain style representation of a target domain image; in order to enable generated new style representations to be different from a source domain style representation of the source domain image and the target domain style representation, enable the new style representations to be different from each other, and enable an image generated by combining the new style representations with the source domain content representation to be semantically consistent with the source domain image, generating multiple new style representations and updating the source domain content representation and the target domain style representation; respectively combining the generated multiple new style representations and the updated target domain style representation with the updated source domain content representation, and respectively generating a first image and a second image; training a target detection model by using the first image, the second image, and the source domain image to obtain a trained target detection model.