High precision, lightweight, and real-time responsiveness are three essential requirements for implementing autonomous driving. In this study, we present an adaptive, real-time, and lightweight multi-task model designed to concurrently address object detection, drivable area segmentation, and lane line segmentation tasks. Specifically, we developed an end-to-end multi-task model with a unified and streamlined segmentation structure. We introduced a learnable parameter that adaptively concatenate features in segmentation necks, using the same loss function for all segmentation tasks. This eliminates the need for customizations and enhances the model's generalization capabilities. We also introduced a segmentation head composed only of a series of convolutional layers, which reduces the inference time. We achieved competitive results on the BDD100k dataset, particularly in visualization outcomes. The performance results show a mAP50 of 81.1% for object detection, a mIoU of 91.0% for drivable area segmentation, and an IoU of 28.8% for lane line segmentation. Additionally, we introduced real-world scenarios to evaluate our model's performance in a real scene, which significantly outperforms competitors. This demonstrates that our model not only exhibits competitive performance but is also more flexible and faster than existing multi-task models. The source codes and pre-trained models are released at https://github.com/JiayuanWang-JW/YOLOv8-multi-task

翻译：高精度、轻量化及实时响应是实现自动驾驶的三个基本要求。本研究提出一种自适应、实时且轻量的多任务模型，旨在同时完成目标检测、可行驶区域分割与车道线分割任务。具体而言，我们构建了一个具有统一且精简分割结构的端到端多任务模型，通过引入可学习参数自适应拼接分割颈部中的特征，并对所有分割任务采用相同损失函数，从而消除定制化需求并提升模型泛化能力。同时，我们设计了仅由系列卷积层构成的分割头，有效降低推理时间。在BDD100k数据集上取得了具有竞争力的结果，尤其在可视化效果方面表现突出：目标检测mAP50达81.1%，可行驶区域分割mIoU达91.0%，车道线分割IoU达28.8%。此外，我们引入真实场景评估模型表现，其性能显著优于竞品。实验表明，该模型不仅表现出色，且较现有多任务模型更具灵活性与速度优势。源代码与预训练模型已发布于https://github.com/JiayuanWang-JW/YOLOv8-multi-task