Object Segmentation and Augmented Visualization Based on Panoramic Image Segmentation

Panoramic images can provide critical information for Advanced Driving Assistance Systems (ADAS), such as parking spaces and surrounding vehicles. However, the vehicle in the bird's-eye view image is severely distorted and incomplete, and the visual information becomes very blurred in some illumination insufficient environments. If the driver cannot see the surrounding environment information, the risk of collision will increase, especially during parking. To better percept the local environment with the help of panoramic images, we use panoramic image segmentation results to construct a virtual surround view monitoring system to provide drivers with clearer perception information. Firstly, a lightweight segmentation network is redesigned based on SegNet, which will improve the accuracy of the segmentation without increasing the model’s inference time. Secondly, we build an augment visualization around view monitor (AV-AVM) system with regards to the segmentation results. All necessary segmentation results will be presented as augmented visualization in AV-AVM systems, such as parking slots and road markings. Compared with the traditional panoramic system, the virtual panoramic surround view system we designed can provide the driver with more intuitive environmental perception information and can be further used to construct an automatic parking map.