Abstract: In the field of intelligent driving, a neural network-based and perspective down-sampling-based landmark
detection method was proposed to accurately detect the road guide signs in real time. This proposed method can
effectively solve the problems of poor real-time performance of traditional detection methods and low detection
accuracy for complex scenes and remote small targets. Firstly, the region of interest for the image was selected for
perspective down-sampling to reduce the near resolution of the road image, reduce the image size, and eliminate the
perspective projection error. Secondly, the YOLOv3-tiny target detection network was enhanced. The boundary frame
clustering of self-built data set was implemented by k-means++. The convolution layer was added to strengthen the
shallow features and enhance the small target representation ability. By changing the fusion scale of feature pyramid,
the prediction output was adjusted to 26×26 and 52×52. Finally, the accuracy rate was elevated from 78% to 99% on
the self-built multi-scene data set, and the model size was reduced from 33.8 MB to 8.3 MB. The results show that a neural network-based and perspective down-sampling-based landmark detection method displays strong robustness,
higher detection accuracy for small targets, and is readily deployable on low-end embedded devices.

Key words: perspective down-sampling, YOLOv3-tiny, landmark detection, data set, k-means++