The parking space detection procedure consists of four stages. First, parking slot markings are detected by estimating parallel line pairs in the AVM images. Second, free spaces are found by recognizing adjacent vehicles using ultrasonic sensor data. 'Vacant Parking Slot Detection in the Around View Image Based on Deep Learning.' Sensors 20.7 (2020): 2138. 'Vision-Based Parking Slot Detection Based on End-to-End Semantic Segmentation Training.' 2020 IEEE International Conference on Consumer Electronics (ICCE). 'End-to-End Trainable One-Stage Parking Slot Detection Integrating. This was developed during one day and the algorithm is pretty stupid. Unfortunately the code is lost. However this is how it was done: 1. Perspective transfo. Jul 12, 2019 From marking-points, parking-slots on I could be easily inferred using geometric rules. DMPR-PS outperforms state-of-the-art competitors on the benchmark dataset with a precision rate of 99.42% and a recall rate of 99.37%, while achieving a real-time detection speed of 12ms per frame on Nvidia Titan Xp. Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net.
December 2020
tl;dr: Parking slot detection by detecting marking point with a CenterNet-like algorithm.
Overall impression
For my future self: Dataset is super important. Your algorithm is only going to evolve to the level your dataset KPI requires it to.
The algorithm only focuses on detecting the marking point detection and did not mention too much about the post-processing needed to combine the marking points to parking slot. It is more general in that it can detect more than T/L-shaped marking points.
The paper is very poorly written, with tons of sloppy annotation and non-standard terminology.
Key ideas
- A coarse-to-fine marking point detection algorithm. Very much like CenterNet.
- The regression also predicts the 'vertex paradigm'. Basically it predicts the pattern of the connectivity among the marking points.
Parking Slot Detection Github Device
Technical details
Parking Slot Detection Github Tool
- Annotated a dataset (~15k images). This is slightly bigger than PS2.0 dataset with 12k images.
- The paper uses L2 loss to supervise the heatmaps and attributes. This is a bit strange as most studies uses focal loss for heatmap prediction and L1 for attribute prediction.
Notes
- Questions and notes on how to improve/revise the current work
