Road Marking Real-Time Detection with a Single Stage Object Detector

Vital guidance information for autonomous driving cars can be extracted from road markings. The main challenges come from the wide range of traffic scenarios, lighting and weather conditions. In this work, we look into the problem of road marking detection with object detection neural networks on the CeyMo Road Marking dataset. We develop an architecture named SE-YOLOv7-tiny, which is based on YOLOv7-tiny, as it is a lightweight object detector that can be deployed on edge devices. To improve model performance, we propose several adjustments to YOLOv7-tiny. Firstly, we replace the Leaky-RELU activation function with Mish. Then we fine-tune data augmentation parameters and introduce into the pipeline the mosaic data augmentation technique. The final adjustment that we propose is to introduce squeeze-and-excitation blocks for features fusion into the neck part of the architecture. We evaluate the inference speed of the obtained models on NVIDIA RTX 2070. The experimental results show that our method achieves a macro F1-score of 89.59% and scenario overall F1-score of 93.32%. Compared with previous state-of-the-art (SSD-Inception-v2) on the object detection task, our model improves macro F1-score by 6.71% and scenario overall F1-score by 8.13%, while achieving twice the FPS.