School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.
Traffic Management Research Institute, Ministry of Public Security, Wuxi 214151, China.
Sensors (Basel). 2020 Dec 23;21(1):27. doi: 10.3390/s21010027.
As an effective means of solving collision problems caused by the limited perspective on board, the cooperative roadside system is gaining popularity. To improve the vehicle detection abilities in such online safety systems, in this paper, we propose a novel multi-sensor multi-level enhanced convolutional network model, called multi-sensor multi-level enhanced convolutional network architecture (MME-YOLO), with consideration of hybrid realistic scene of scales, illumination, and occlusion. MME-YOLO consists of two tightly coupled structures, i.e., the enhanced inference head and the LiDAR-Image composite module. More specifically, the enhanced inference head preliminarily equips the network with stronger inference abilities for redundant visual cues by attention-guided feature selection blocks and anchor-based/anchor-free ensemble head. Furthermore, the LiDAR-Image composite module cascades the multi-level feature maps from the LiDAR subnet to the image subnet, which strengthens the generalization of the detector in complex scenarios. Compared with YOLOv3, the enhanced inference head achieves a 5.83% and 4.88% mAP improvement on visual dataset LVSH and UA-DETRAC, respectively. Integrated with the composite module, the overall architecture gains 91.63% mAP in the collected Road-side Dataset. Experiments show that even under the abnormal lightings and the inconsistent scales at evening rush hours, the proposed MME-YOLO maintains reliable recognition accuracy and robust detection performance.
作为解决因船上视角有限而导致的碰撞问题的有效手段，合作路边系统越来越受欢迎。为了提高在线安全系统中的车辆检测能力，本文提出了一种新的多传感器多层次增强卷积网络模型，称为多传感器多层次增强卷积网络架构（MME-YOLO），考虑了混合真实场景的尺度、光照和遮挡。MME-YOLO 由两个紧密耦合的结构组成，即增强推理头和 LiDAR-Image 复合模块。更具体地说，增强推理头通过注意力引导特征选择块和基于锚点/无锚点的集成头初步为网络配备更强的推理能力，以利用冗余的视觉线索。此外，LiDAR-Image 复合模块级联来自 LiDAR 子网的多层次特征图到图像子网，这增强了检测器在复杂场景中的泛化能力。与 YOLOv3 相比，增强推理头在视觉数据集 LVSH 和 UA-DETRAC 上分别实现了 5.83%和 4.88%的 mAP 提高。集成复合模块后，所提出的整体架构在收集的路边数据集上获得了 91.63%的 mAP。实验表明，即使在异常光照和傍晚高峰时段不一致的情况下，所提出的 MME-YOLO 仍能保持可靠的识别精度和鲁棒的检测性能。
