# FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System Source: [FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:59:21.344Z Authors: Pullarao Maddu, Wayne Doherty, Ganesh Sistu, Isabelle Leang, Michal Uricar, Sumanth Chennupati, Hazem Rashed, Jonathan Horgan, Ciaran Hughes, Senthil Yogamani Published: 2019-12-23T19:11:50.000Z ## Abstract Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360{\\deg} 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 \\cite{yogamani2019woodscape}. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()