基于深度学习与数据回归的智能车单目测距方法

[Objective] In view of the problems of low accuracy and poor generality of monocular ranging in autonomous driving, a monocular ranging method based on deep learning and regression was proposed. [Method] An improved YOLOv5 object detection algorithm was proposed. First, the K-means++ algorithm was used to re-cluster the prior boxes, optimizing the initial cluster centers. Second, an attention mechanism was introduced into the backbone network, effectively enhancing the feature extraction capabilities. Meanwhile, the original loss function was replaced with the E-IOU loss function, significantly reducing the target localization errors. Then, the monocular ranging model was constructed based on the improved object detection algorithm. The model established several regression equations based on the diagonal lengths of different detected objects and their real distances from camera, increasing the number of pixels per unit image, and enhancing the detection precision. Finally, the model used Kalman filtering to integrate vehicle motion data, achieving the dual-data fusion correction for ranging. [ Result ] The detection accuracy of improved algorithm increases by 0. 68% compared with the original YOLOv5 with noticeable reduction in false positives. The average relative error of proposed algorithm is 3. 67% within the range of 10 - 100 m, and 3. 24% within the range of 30 - 100 m. The entire algorithm is accelerated by using NVIDIA’ s TensorRT. The inference speed for a single image reaches 12 ms. That is 5 times faster than that with the unaccelerated version, and 4 times faster than that with the binocular algorithm. The proposed method achieves optimal performance in ranging scenarios beyond 30 m. [ Conclusion] The proposed algorithm can meet the ranging requirements for both intelligent and non-intelligent vehicles with general accuracy needs.