标题： TriLiteNet：多任务视觉感知的轻量级模型 显示英文标题

标题： TriLiteNet: Lightweight Model for Multi-Task Visual Perception

摘要： 高效感知模型对于高级驾驶辅助系统（ADAS）至关重要，因为这些应用需要快速处理和响应以确保在现实环境中的安全性和有效性。为解决此类感知模型的实时执行需求，本研究引入了TriLiteNet模型。该模型可以同时处理与全景驾驶感知相关的多个任务。TriLiteNet旨在优化性能的同时保持低计算成本。在BDD100k数据集上的实验结果表明，该模型在三个关键任务中表现出色：车辆检测、可行驶区域分割和车道线分割。具体而言，TriLiteNet_{基础}在车辆检测中实现了85.6%的召回率，可行驶区域分割的平均交并比（mIoU）为92.4%，车道线分割的准确率为82.3%，仅使用2.35M参数和7.72 GFLOPs的计算成本。我们提出的模型包含一个仅有0.14M参数的微型配置，提供了计算需求最小的多任务解决方案。在嵌入式设备上评估了延迟和功耗，TriLiteNet在两种配置下在推理过程中表现出低延迟和合理的功耗。通过平衡性能、计算效率和可扩展性，TriLiteNet为现实世界的自动驾驶应用提供了一个实用且可部署的解决方案。代码可在https://github.com/chequanghuy/TriLiteNet获取。 显示英文摘要

摘要： 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.