Abstract
Fisheye lens cameras are widely used in such applications where a large field of view (FOV) is necessary. A large FOV can provide an enhanced understanding of the surrounding environment and can be an effective solution for detecting the objects in automotive applications. However, this comes with the cost of strong radial distortions and irregular size of objects depending on the location in an image. Therefore, we propose a new fisheye image warping method called Expandable Spherical Projection to expand the center and boundary regions in which smaller objects are mostly located. The proposed method produces undistorted objects especially in the image boundary and a less unwanted background in the bounding boxes. Additionally, we propose three multi-scale feature concatenation methods and provide the analysis of the influence from the three concatenation methods in a real-time object detector. Multiple fisheye image datasets are employed to demonstrate the effectiveness of the proposed projection and feature concatenation methods. From the experimental results, we find that the proposed Expandable Spherical projection and the LCat feature concatenation yield the best AP performance, which is up to 4.7% improvement compared to the original fisheye image datasets and the baseline model.
Highlights
Comprehensive information about the environment is one of the important properties of advanced driver-assistance system (ADAS)
Instead of using θ in (7), we suggest the expression of θproposed which is the multiplication of expansion weight w and θ in expandable spherical projection, as shown in (11)
The result shows that the model achieves the highest performance with the fisheye image dataset and the projected image from the spherical-based method gives no positive effect in the accuracy
Summary
Comprehensive information about the environment is one of the important properties of advanced driver-assistance system (ADAS). During the last few years, deep-learning based methods show the most promising performance with the development of open-source frameworks [1,2,3,4,5]. This approach requires a relatively large computational resource, but modern hardware can be adapted to real-time detection. A combination of sensors, such as cameras, radar, lidar, and GPU, are used to collect the data around the environment and extract the relevant information in the perception stage. In a low-cost sensor setup, 2D cameras with a large field of view (FOV) can efficiently cover a large area around the vehicle and ensure the safety of the autonomous driving. The fisheye camera can obtain visual information with a more than 180◦ field of view, the fisheye camera is widely used in ground, aerial, and underwater autonomous robot as well as surveillance [6,7,8]
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