# CNN-based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction

arXiv: https://arxiv.org/abs/2108.06185 · HTML: https://mlsanity.com/paper/2108.06185/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Instance-aware Semantic Segmentation via Multi-task Network Cascades](https://mlsanity.com/paper/1512.04412/) (2015)
- [A Survey of Deep Learning-based Object Detection](https://mlsanity.com/paper/1907.09408/) (2019)
- [Attentional Graph Neural Network for Parking-slot Detection](https://mlsanity.com/paper/2104.02576/) (2021)
- [An Anchor-Free Region Proposal Network for Faster R-CNN based Text Detection Approaches](https://mlsanity.com/paper/1804.09003/) (2018)
- [End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information](https://mlsanity.com/paper/2003.02445/) (2020)

## Built on by (most cited first, 2 of 2)

- [Holistic Parking Slot Detection with Polygon-Shaped Representations](https://mlsanity.com/paper/2310.11629/) (2023)
- [Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline](https://mlsanity.com/paper/2509.13133/) (2025)

## Abstract (arXiv)

Autonomous parking systems start with the detection of available parking slots. Parking slot detection performance has been dramatically improved by deep learning techniques. Deep learning-based object detection methods can be categorized into one-stage and two-stage approaches. Although it is well-known that the two-stage approach outperforms the one-stage approach in general object detection, they have performed similarly in parking slot detection so far. We consider this is because the two-stage approach has not yet been adequately specialized for parking slot detection. Thus, this paper proposes a highly specialized two-stage parking slot detector that uses region-specific multi-scale feature extraction. In the first stage, the proposed method finds the entrance of the parking slot as a region proposal by estimating its center, length, and orientation. The second stage of this method designates specific regions that most contain the desired information and extracts features from them. That is, features for the location and orientation are separately extracted from only the specific regions that most contain the locational and orientational information. In addition, multi-resolution feature maps are utilized to increase both positioning and classification accuracies. A high-resolution feature map is used to extract detailed information (location and orientation), while another low-resolution feature map is used to extract semantic information (type and occupancy). In experiments, the proposed method was quantitatively evaluated with two large-scale public parking slot detection datasets and outperformed previous methods, including both one-stage and two-stage approaches.