Multi-Feature Target Detection via Integrated Deep Learning Models

The patent combines multiple different deep learning models into a unified detection system. Each model extracts different types of image features (e.g., color, texture, shape) from the same image, and their detection results are integrated through a fusion mechanism. This merging approach improves detection accuracy by compensating for the limitations of individual models while maintaining manageable system complexity through modular architecture.

The patent creates a composite detection system that integrates multiple types of image features extracted by different models. Similar to composite materials combining different substances to achieve superior properties, this system combines color features, texture features, and shape features from multiple models to create a more robust and accurate detection system that overcomes the weaknesses of any single feature type.

The patent segments the detection task into multiple specialized sub-tasks, where each deep learning model is responsible for extracting specific types of image features (e.g., one model for color features, another for texture features). This segmentation allows each model to be optimized for its specific function, improving feature extraction accuracy while enabling parallel processing that can mitigate the increase in computational complexity.

The patent designs multiple deep learning models that, while extracting different feature types, all operate on the same input image and follow a similar architectural framework. This multi-functionality approach allows the system to leverage common computational resources and processing pipelines across different feature extraction tasks, reducing the overall computational burden compared to completely separate processing systems.