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

arXiv: https://arxiv.org/abs/1912.11066 · HTML: https://mlsanity.com/paper/1912.11066/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 6 of 6)

- [OmniDet: Surround View Cameras based Multi-task Visual Perception Network for Autonomous Driving](https://mlsanity.com/paper/2102.07448/) (2021)
- [Surround-view Fisheye Camera Perception for Automated Driving: Overview, Survey and Challenges](https://mlsanity.com/paper/2205.13281/) (2022)
- [Near-field Perception for Low-Speed Vehicle Automation using Surround-view Fisheye Cameras](https://mlsanity.com/paper/2103.17001/) (2021)
- [RED: A Systematic Real-Time Scheduling Approach for Robotic Environmental Dynamics](https://mlsanity.com/paper/2308.15368/) (2023)
- [Disentangling and Vectorization: A 3D Visual Perception Approach for Autonomous Driving Based on Surround-View Fisheye Cameras](https://mlsanity.com/paper/2107.08862/) (2021)
- [Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving](https://mlsanity.com/paper/2105.07930/) (2021)

## Abstract (arXiv)

Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360deg 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 citeyogamani2019woodscape.