Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection

Haolei Chen

doi:10.47297/taposatWSP2633-456911.20250611

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Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection

6卷第11期

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Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection
Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection
Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
2025年6卷第11期页码：55-60
纸质出版：2025-11-30
DOI：10.47297/taposatWSP2633-456911.20250611
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Haolei Chen. (2025). Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection. 科技理论与实践（英文版）, 6(11), 55-60.

Chen Haolei.Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection[J].Theory and Practice of Science and Technology,2025,06(11):55-60.
Haolei Chen. (2025). Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection. 科技理论与实践（英文版）, 6(11), 55-60. DOI： 10.47297/taposatWSP2633-456911.20250611.

Chen Haolei.Park-YOLO: An Efficient and Lightweight YOLOv8-based Model for Parking Slot Detection[J].Theory and Practice of Science and Technology,2025,06(11):55-60. DOI： 10.47297/taposatWSP2633-456911.20250611.

摘要

Abstract

Visual detection of parking spaces plays a vital role in contemporary intelligent transportation frameworks. However

achieving a balance between high precision and low computational cost remains a major hurdle

particularly when deploying models on edge computing devices. Real-world parking scenarios often involve difficult conditions such as poor illumination

vehicle occlusion

and small-sized targets

which limits the performance of current algorithms. To mitigate these problems

we introduce Park-YOLO

a novel

lightweight

and efficient parking slot detection model built upon the state-of-the-art YOLOv8 framework. Our approach introduces a key innovation: we incorporate the parameter-free SimAM attention module to strengthen the network's capacity to highlight discriminative features

particularly for small or partially hidden targets

without imposing a substantial rise in computational complexity. We will validate our model on a challenging

self-curated parking lot dataset. We anticipate that Park-YOLO will exhibit an optimal balance between detection precision (mAP)

inference speed (FPS)

and parameter efficiency

outperforming the standard YOLOv8s baseline and other comparable lightweight detectors. This work presents a pragmatic and robust approach for developing real-time

robust

and deployable smart parking systems.

关键词

Keywords

references

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