Traffic Sign Detection With RT-DETR for Small-Object Accuracy

The patent segments the feature processing into multiple stages with different resolutions. It introduces parallel feature processing branches that handle features at different scales, allowing the model to simultaneously process both large and small targets effectively. The feature pyramid structure divides features into different levels, each optimized for detecting targets of specific size ranges.

The patent applies local quality by using different processing strategies for different regions of the feature map. It introduces task-specific attention mechanisms that adaptively adjust feature processing based on local characteristics, applying stronger down-sampling and feature extraction in regions containing small objects while maintaining global semantic context for larger targets.

The patent performs preliminary feature extraction and processing before the main detection task. It pre-processes input images through multiple convolutional layers to extract hierarchical features, and pre-computes attention maps and feature embeddings. This preliminary action prepares the model for faster convergence during training by initializing features in a more favorable state.

The patent maintains continuous feature processing throughout the network architecture. It introduces residual connections that allow gradient flow and feature propagation across all layers, ensuring that useful information continues to flow through the network during both training and inference. The parallel processing branches operate continuously to maintain feature representations at all scales simultaneously.