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TriLiteNet: Lightweight Model for Multi-Task Visual Perception
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DOI：10.1109/ACCESS.2025.3552088.png)
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Abstract
En 中文
Efficient perception models are essential for Advanced Driver Assistance Systems (ADAS), as these applications require rapid processing and response to ensure safety and effectiveness in real-world environments. To address the real-time execution needs of such perception models, this study introduces the TriLiteNet model. This model can simultaneously manage multiple tasks related to panoramic driving perception. TriLiteNet is designed to optimize performance while maintaining low computational costs. Experimental results on the BDD100k dataset demonstrate that the model achieves competitive performance across three key tasks: vehicle detection, drivable area segmentation, and lane line segmentation. Specifically, the TriLiteNet(base) demonstrated a recall of 85.6% for vehicle detection, a mean Intersection over Union (mIoU) of 92.4% for drivable area segmentation, and an Acc of 82.3% for lane line segmentation with only 2.35M parameters and a computational cost of 7.72 GFLOPs. Our proposed model includes a tiny configuration with just 0.14M parameters, which provides a multi-task solution with minimal computational demand. Evaluated for latency and power consumption on embedded devices, TriLiteNet in both configurations shows low latency and reasonable power during inference. By balancing performance, computational efficiency, and scalability, TriLiteNet offers a practical and deployable solution for real-world autonomous driving applications. Code is available at https://github.com/chequanghuy/TriLiteNet.
Keywords:
Computational modeling
Multitasking
Vehicle detection
Feature extraction
Computational efficiency
Autonomous vehicles
Head
Decoding
Real-time systems
Computer architecture
Multi-task learning
driving perception
BDD100K
light-weight model
embedded devices
TwinLiteNet
TriLiteNet
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W