Liu Ri, Yang Shubin, Tang Wansha, Yuan Jie, Chan Qiqing, Yang Yunchuan
School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China.
Sensors (Basel). 2024 Aug 28;24(17):5552. doi: 10.3390/s24175552.
In autonomous driving, environmental perception technology often encounters challenges such as false positives, missed detections, and low accuracy, particularly in detecting small objects and complex scenarios. Existing algorithms frequently suffer from issues like feature redundancy, insufficient contextual interaction, and inadequate information fusion, making it difficult to perform multi-task detection and segmentation efficiently. To address these challenges, this paper proposes an end-to-end multi-task environmental perception model named YOLO-Mg, designed to simultaneously perform traffic object detection, lane line detection, and drivable area segmentation. First, a multi-stage gated aggregation network (MogaNet) is employed during the feature extraction process to enhance contextual interaction by improving diversity in the channel dimension, thereby compensating for the limitations of feed-forward neural networks in contextual understanding. Second, to further improve the model's accuracy in detecting objects of various scales, a restructured weighted bidirectional feature pyramid network (BiFPN) is introduced, optimizing cross-level information fusion and enabling the model to handle object detection at different scales more accurately. Finally, the model is equipped with one detection head and two segmentation heads to achieve efficient multi-task environmental perception, ensuring the simultaneous execution of multiple tasks. The experimental results on the BDD100K dataset demonstrate that the model achieves a mean average precision (mAP50) of 81.4% in object detection, an Intersection over Union (IoU) of 28.9% in lane detection, and a mean Intersection over Union (mIoU) of 92.6% in drivable area segmentation. The tests conducted in real-world scenarios show that the model performs effectively, significantly enhancing environmental perception in autonomous driving and laying a solid foundation for safer and more reliable autonomous driving systems.
在自动驾驶中，环境感知技术常常面临误报、漏检和低精度等挑战，尤其是在检测小物体和复杂场景时。现有算法经常存在特征冗余、上下文交互不足和信息融合不充分等问题，难以高效地执行多任务检测和分割。为应对这些挑战，本文提出了一种名为YOLO-Mg的端到端多任务环境感知模型，旨在同时执行交通目标检测、车道线检测和可行驶区域分割。首先，在特征提取过程中采用了多级门控聚合网络（MogaNet），通过提高通道维度的多样性来增强上下文交互，从而弥补前馈神经网络在上下文理解方面的局限性。其次，为进一步提高模型在检测各种尺度物体时的准确性，引入了一种重构的加权双向特征金字塔网络（BiFPN），优化跨层信息融合，使模型能够更准确地处理不同尺度的目标检测。最后，该模型配备了一个检测头和两个分割头，以实现高效的多任务环境感知，确保多个任务同时执行。在BDD100K数据集上的实验结果表明，该模型在目标检测中的平均精度均值（mAP50）达到81.4%，在车道检测中的交并比（IoU）为28.9%，在可行驶区域分割中的平均交并比（mIoU）为92.6%。在实际场景中进行的测试表明，该模型运行有效，显著增强了自动驾驶中的环境感知能力，为更安全、更可靠的自动驾驶系统奠定了坚实基础。
