基于YOLOX的轻量级毫米波雷达和相机融合检测算法

RV-YOLOX：Lightweight radar and camera fusion detection algorithm

摘要:

路侧端交通目标感知常用的传感器有相机、毫米波雷达和激光雷达。激光雷达能感知到3D信息，但成本昂贵且在雨、雾、灰尘天气下易受干扰。相机的价格低劣，感知到的信息也相当丰富，但受光照和杂波干扰比较严重。毫米波雷达具有全天时、全天候工作的优势，但并不擅长检测静止的目标。为了满足交通系统全天时、全天候高效准确的感知需求，本文提出了融合检测框架RV-YOLOX，通过有效融合相机和毫米波雷达传感器的信息，取得了优于单源传感器的检测效果。RV-YOLOX中设计的雷达空间注意力模块吸纳了级联融合和逐元素相加融合的特点，可以通过将雷达的空间信息传递给视觉特征，促使其提取更加有效的信息流。此外，本文还通过结构重参数化的方式对RV-YOLOX进行了轻量化处理，使其能够在保持原有精度的同时达到更快的推理速度。最后，在自制数据集和NuScenes数据集上训练并测试算法，RV-YOLOX相比YOLOX算法ap指标能提升约3~4个点左右，且轻量级RV-YOLOX也能在提高推理速度的同时获得与RV-YOLOX相当的检测精度。

Abstract:

Commonly used sensors for roadside traffic target perception include cameras, millimeter-wave radars, and lidars. Lidar can perceive 3D information, but it is expensive and susceptible to interference in rain, fog, and dusty weather. The price of the camera is low, and the information it perceives is quite rich, but it is seriously disturbed by light and clutter. Millimeter-wave radar has the advantage of working all day and all day, but it is not good at detecting stationary targets. In order to meet the all-day and all-weather efficient and accurate perception requirements of the traffic system, this paper proposes a fusion detection framework RV-YOLOX, which achieves better detection results than single-source sensors by effectively fusing the information of cameras and millimeter-wave radar sensors. The radar spatial attention module designed in RV-YOLOX absorbs the characteristics of cascade fusion and element-by-element addition fusion, which can facilitate the extraction of more effective information flow by transferring the spatial information of radar to visual features. In addition, this paper also lightweights RV-YOLOX by means of structural re-parameterization, which enables it to achieve faster inference speed while maintaining the original accuracy. Finally, the algorithm is trained and tested on the self-made dataset and NuScenes dataset. Compared with the YOLOX algorithm, the ap index of RV-YOLOX can be improved by about 3~4 points, and the lightweight RV-YOLOX can also improve the inference speed at the same time. Obtain detection accuracy comparable to RV-YOLOX.