Close-Following Detection With Multi-Task Vision and Vehicle Speed

The patent implements a multi-task learning framework where a single neural network performs multiple detection functions simultaneously: close following detection, camera obstruction detection, lane detection, object detection, and distance estimation. This multi-functional approach improves detection accuracy across all tasks while avoiding the need for separate classifier models for each function.

The patent combines multiple independent detection tasks into a unified neural network architecture with shared backbone features. The joint loss function aggregates losses from all prediction heads, merging the training process of multiple classifiers into a single optimized model that leverages common features across tasks.

The neural network is designed as a universal detector that handles multiple detection tasks (close following, obstruction, lane, object, distance) within a single model structure. This eliminates the need for deploying multiple independent classifiers, reducing system complexity while maintaining high detection accuracy through shared feature extraction.

While the model is unified, it segments different detection functions into separate prediction heads that branch from the shared backbone. This segmentation allows each task to have specialized processing while benefiting from common feature representation, balancing specialization with overall system efficiency.

The patent merges multiple task-specific loss functions into a single joint loss function that aggregates information from all detection tasks. This unified loss function ensures that the model learns features beneficial for all tasks simultaneously, preventing information loss that would occur with separate training processes while maintaining manageable training complexity through a cohesive optimization framework.

The shared backbone network in the multi-task model learns universal features that are beneficial across all detection tasks. This feature sharing reduces information loss by ensuring that learned representations are optimized for multiple purposes, while the joint training process maintains simplicity through unified gradient descent optimization.