Multi-Task Target Detection With Staged Backbone Training
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Solution Overview
Problem
The reliability of multi-task target detection in autonomous driving is affected by high task conflicts, low convergence speed, and poor model training effects due to the use of multi-source data sets with varying task distributions and large numbers of tasks.
Innovation Solution
A multi-task target detection method involving a step-by-step training process, utilizing a shared main backbone network, branch backbone networks for different task groups, and individual head networks, with staged training on selected and fixed parameters to optimize model parameters and reduce task conflicts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a multi-task learning model uses a single backbone network to extract depth features for multiple tasks, then computing resources are reduced and delay is minimized, but task conflicts increase and training reliability deteriorates when using multi-source data sets with large numbers of tasks
Solution Approach 1:
The patent divides the backbone network into a first backbone network and a second backbone network. The first backbone network processes common features shared across multiple tasks, while the second backbone network processes task-specific features. This segmentation reduces task conflicts by isolating different task requirements into separate processing paths while maintaining computational efficiency through shared common features.
2Adaptability or versatility
If multi-source data sets with large numbers of tasks are used for training, then the model's adaptability improves, but task conflicts increase and convergence speed decreases
Solution Approach 1:
The patent segments the training process into two stages: first-stage training using only the first backbone network with a subset of tasks to establish common features, and second-stage training using both backbone networks with all tasks to optimize task-specific features. This segmentation enables the model to handle large numbers of tasks from multi-source data sets while maintaining fast convergence by progressively building knowledge rather than attempting to learn all tasks simultaneously.
3Adaptability or versatility
If all tasks are trained simultaneously using multi-source data sets, then the model's versatility improves, but training time increases and convergence speed decreases
Solution Approach 1:
The patent performs preliminary action by conducting first-stage training before second-stage training. In the first stage, the model is trained on a subset of tasks using only the first backbone network to establish common feature representations. This preliminary training prepares the model for subsequent second-stage training where all tasks are processed using both backbone networks. This preliminary action significantly reduces the time required for full training by pre-establishing common features that can be reused across all tasks.
Data Source
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AI summary
The disclosure provides a multi-task target detection method and device, an autonomous driving system, and a storage medium. The method includes: selecting some of tasks in each task group to perform first-stage training on a model to be trained, to obtain an initial trained model, which prevents task conflicts, thereby increasing the convergence speed; fixing parameters of a main backbone network in the initial trained model, and performing second-stage training on the initial trained model by using all tasks in each task group, to obtain an intermediate trained model, which implements the training of different tasks in a uni-source data set or a multi-source data set without mutual interference, thereby shortening the training time; and releasing parameters of the main backbone network in the intermediate trained model, performing third-stage training on the intermediate trained model by using all tasks in all task groups to obtain a multi-task target detection model, inputting an image under detection that contains a target detection object into the multi-task target detection model, and detecting and recognizing the target detection object, which improves the reliability of multi-task target detection.