Lu Eric Hsueh-Chan, Hsieh Yi-Chun
Department of Geomatics, National Cheng Kung University, No. 1, University Rd., Tainan 701, Taiwan.
Sensors (Basel). 2024 Dec 18;24(24):8080. doi: 10.3390/s24248080.
With the rapid development of the autonomous vehicles industry, there has been a dramatic proliferation of research concerned with related works, where road markings detection is an important issue. When there is no public open data in a field, we must collect road markings data and label them by ourselves manually, which is huge labor work and takes lots of time. Moreover, object detection often encounters the problem of small object detection. The detection accuracy often decreases when the detection distance increases. This is primarily because distant objects on the road take up few pixels in the image and object scales vary depending on different distances and perspectives. For the sake of solving the issues mentioned above, this paper utilizes a virtual dataset and open dataset to train the object detection model and cross-field testing in the field of Taiwan roads. In order to make the model more robust and stable, the data augmentation method is employed to generate more data. Therefore, the data are increased through the data augmentation method and homography transformation of images in the limited dataset. Additionally, Inverse Perspective Mapping is performed on the input images to transform them into the bird's eye view, which solves the "small objects at far distance" problem and the "perspective distortion of objects" problem so that the model can clearly recognize the objects on the road. The model testing on the front-view images and bird's eye view images also shows a remarkable improvement of accuracy by 18.62%.
随着自动驾驶汽车行业的快速发展，有关相关工作的研究急剧增加，其中道路标线检测是一个重要问题。当一个领域没有公开的开放数据时，我们必须自己手动收集道路标线数据并进行标注，这是一项巨大的劳动工作，并且需要花费大量时间。此外，目标检测经常遇到小目标检测的问题。当检测距离增加时，检测准确率通常会下降。这主要是因为道路上远处的物体在图像中占据的像素很少，并且目标尺度会根据不同的距离和视角而变化。为了解决上述问题，本文利用虚拟数据集和开放数据集来训练目标检测模型，并在台湾道路领域进行跨领域测试。为了使模型更健壮和稳定，采用数据增强方法来生成更多数据。因此，通过数据增强方法和对有限数据集中图像的单应性变换来增加数据。此外，对输入图像进行逆透视映射，将其转换为鸟瞰图，解决了“远处小目标”问题和“目标透视变形”问题，从而使模型能够清晰地识别道路上的目标。对前视图图像和鸟瞰图图像的模型测试也显示准确率显著提高了18.62%。
