Multi-Task Learning for Fatigue Detection and Face Recognition of Drivers via Tree-Style Space-Channel Attention Fusion Network
CoRR(2024)
Abstract
In driving scenarios, automobile active safety systems are increasingly
incorporating deep learning technology. These systems typically need to handle
multiple tasks simultaneously, such as detecting fatigue driving and
recognizing the driver's identity. However, the traditional parallel-style
approach of combining multiple single-task models tends to waste resources when
dealing with similar tasks. Therefore, we propose a novel tree-style multi-task
modeling approach for multi-task learning, which rooted at a shared backbone,
more dedicated separate module branches are appended as the model pipeline goes
deeper. Following the tree-style approach, we propose a multi-task learning
model for simultaneously performing driver fatigue detection and face
recognition for identifying a driver. This model shares a common feature
extraction backbone module, with further separated feature extraction and
classification module branches. The dedicated branches exploit and combine
spatial and channel attention mechanisms to generate space-channel
fused-attention enhanced features, leading to improved detection performance.
As only single-task datasets are available, we introduce techniques including
alternating updation and gradient accumulation for training our multi-task
model using only the single-task datasets. The effectiveness of our tree-style
multi-task learning model is verified through extensive validations.
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