To address the high structural similarity and low feature importance of the roadside image background, the object detection algorithm is improved based on the mechanism of Single Shot Multibox Detector (SSD). By introducing data preprocessing module and lightweight convolutional attention module, and adjusting the position of the attention module in the algorithm, an optimized roadside detection algorithm is established. The roadside object detection task is performed for daytime, nighttime scenarios and different levels of traffic flow, and the experimental results on the vehicle-road collaborative public dataset DAIR-V2X show that adding the attention module after the third pooling layer where the optimized algorithm extracts image features results in a 1.67% improvement in accuracy metrics mAP@0.5:0.95 with only a 2 FPS loss in detection speed, which can meet the speed and accuracy requirements of roadside object detection tasks and enhance the perception capability of the vehicle side.

针对路端图像背景高结构相似性及低特征重要性的特点, 基于单发多框检测器(SSD)的运行机制, 通过引入数据预处理和轻量级卷积注意力模块, 并调整注意力模块在算法中的位置, 建立最优化的路端检测算法。在车路协同公开数据集DAIR-V2X上进行日间和夜间场景以及不同交通流量下的路端目标检测, 结果表明, 在提取图像特征的第3级池化层后添加注意力模块, 优化算法的精度指标mAP@0.5:0.95可以获得1.67%的提升, 且仅损失2 FPS的检测速度, 能够满足路端目标检测任务的精度与速度需求, 有效地增强车端的目标识别能力。