FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System.

Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss the design and implementation of an automated parking system from the perspective of camera based deep learning algorithms. We provide a holistic overview of an industrial system covering the embedded system, use cases and the deep learning architecture. We demonstrate a real-time multi-task deep learning network called FisheyeMultiNet, which detects all the necessary objects for parking on a low-power embedded system. FisheyeMultiNet runs at 15 fps for 4 cameras and it has three tasks namely object detection, semantic segmentation and soiling detection. To encourage further research, we release a partial dataset of 5,000 images containing semantic segmentation and bounding box detection ground truth via WoodScape project [Yogamani et al., 2019]

Detection of Empty/Occupied States of Parking Slots in Multicamera system using Mask R-CNN Classifier

A fast growth of vehicles in big cities has an impact of arising road loads and difficulty of finding empty parking spaces. One solution to cope with the problem is to develop a parking management system which can provide useful information of available parking spaces to the potential users. This paper discusses about a new multicamera arrangement and the function to evaluate the empty/occupied states of the parking slots, as an alternative solution to the existing single camera system, The system adopted Mask R-CNN for its classifier, because of its capability to provide the polygon outputs for its detected objects, compared with the existing bounding box outputs provided by other classifiers. The proposed function has optimized the available information from all cameras, by considering the relative position of each camera to the parking spaces, and also capable of overcoming occlusion problem occurs in some cameras, The experiment shows that the capability of overcoming the occlusion problem has been validated, and its performance to evaluate the empty/occupied states of the parking slots was better than the single camera system to a certain threshold

LineMarkNet: Line Landmark Detection for Valet Parking

We aim for accurate and efficient line landmark detection for valet parking, which is a long-standing yet unsolved problem in autonomous driving. To this end, we present a deep line landmark detection system where we carefully design the modules to be lightweight. Specifically, we first empirically design four general line landmarks including three physical lines and one novel mental line. The four line landmarks are effective for valet parking. We then develop a deep network (LineMarkNet) to detect line landmarks from surround-view cameras where we, via the pre-calibrated homography, fuse context from four separate cameras into the unified bird-eye-view (BEV) space, specifically we fuse the surroundview features and BEV features, then employ the multi-task decoder to detect multiple line landmarks where we apply the center-based strategy for object detection task, and design our graph transformer to enhance the vision transformer with hierarchical level graph reasoning for semantic segmentation task. At last, we further parameterize the detected line landmarks (e.g., intercept-slope form) whereby a novel filtering backend incorporates temporal and multi-view consistency to achieve smooth and stable detection. Moreover, we annotate a large-scale dataset to validate our method. Experimental results show that our framework achieves the enhanced performance compared with several line detection methods and validate the multi-task network's efficiency about the real-time line landmark detection on the Qualcomm 820A platform while meantime keeps superior accuracy, with our deep line landmark detection system.Comment: 29 pages, 12 figure

Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras

Cameras are the primary sensor in automated driving systems. They provide high information density and are optimal for detecting road infrastructure cues laid out for human vision. Surround-view camera systems typically comprise of four fisheye cameras with 190{\deg}+ field of view covering the entire 360{\deg} around the vehicle focused on near-field sensing. They are the principal sensors for low-speed, high accuracy, and close-range sensing applications, such as automated parking, traffic jam assistance, and low-speed emergency braking. In this work, we provide a detailed survey of such vision systems, setting up the survey in the context of an architecture that can be decomposed into four modular components namely Recognition, Reconstruction, Relocalization, and Reorganization. We jointly call this the 4R Architecture. We discuss how each component accomplishes a specific aspect and provide a positional argument that they can be synergized to form a complete perception system for low-speed automation. We support this argument by presenting results from previous works and by presenting architecture proposals for such a system. Qualitative results are presented in the video at https://youtu.be/ae8bCOF77uY.Comment: Accepted for publication at IEEE Transactions on Intelligent Transportation System

Surround-view Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-insensitive Multi-task Framework

Surround-view fisheye perception under valet parking scenes is fundamental and crucial in autonomous driving. Environmental conditions in parking lots perform differently from the common public datasets, such as imperfect light and opacity, which substantially impacts on perception performance. Most existing networks based on public datasets may generalize suboptimal results on these valet parking scenes, also affected by the fisheye distortion. In this article, we introduce a new large-scale fisheye dataset called Fisheye Parking Dataset(FPD) to promote the research in dealing with diverse real-world surround-view parking cases. Notably, our compiled FPD exhibits excellent characteristics for different surround-view perception tasks. In addition, we also propose our real-time distortion-insensitive multi-task framework Fisheye Perception Network (FPNet), which improves the surround-view fisheye BEV perception by enhancing the fisheye distortion operation and multi-task lightweight designs. Extensive experiments validate the effectiveness of our approach and the dataset's exceptional generalizability.Comment: 12 pages, 11 figure