Abstract: Artificial intelligence has been applied to various aspects of life, and the emergence of autonomous driving technology is a concrete manifestation of artificial intelligence in the automotive field. In order to detect complex targets on the road, a target detection algorithm based on linear frequency modulation continuous wave and image collaboration is adopted in the study. By analyzing the linear frequency modulation continuous wave and obtaining information on target orientation, distance, and velocity, a target detection model based on the YOLOv7 algorithm is proposed to detect complex targets on the road. The experimental results show that the target detection model has better detection performance for targets with lower speed, closer distance, and smaller angle, demonstrating better detection performance. Selecting different iterations to test the performance of the model, when the number of iterations reaches 600, the loss function value of the YOLOv7 algorithm model is 0.05, which is significantly lower than other algorithms.

Key words: YOLO algorithm, linear frequency modulation continuous wave, target detection, SSD algorithm, image collaboration

摘要： 人工智能已经运用到生活中的各个方面,自动驾驶技术的出现就是人工智能在汽车领域的具体表现。为了对道路上的复杂目标进行检测,文中采用了一种基于线性调频连续波与图像协同的目标检测算法。通过对线性调频连续波进行分析,对目标方位、距离和速度的信息获取,提出了基于YOLOv7算法的目标检测模型,对道路上的复杂目标进行检测。实验结果表明,该目标检测模型对速度较低、距离较近、角度较小的目标有着更好的检测效果,表现出更好的检测性能。选取不同迭代次数对模型性能进行检测,当迭代次数达到600次时,YOLOv7算法模型的损失函数值为0.05,明显低于其他算法。

关键词: YOLO算法, 线性调频连续波, 目标检测, SSD算法, 图像协同