Parking Space Detection

Introduction

🚗 Revolutionizing Urban Parking: Arducam's AI-Powered Space Detection Solution

As cities densify, parking scarcity escalates. Traditional systems—reliant on manual counts or basic sensors—fail to address soaring demand, causing plenty of vacancy misreporting and chronic congestion.

🤖 II. Arducam's Breakthrough: Real-Time Vision Intelligence

Core Solution:

Arducam's Parking Space Detection solution uses the yolov8n pklot model to identify the occupancy of parking spaces in the parking lot in real time, realize intelligent management of the parking lot, improve parking space utilization, alleviate parking pressure, and provide car owners with a convenient and efficient parking experience.

SONY IMX500 Ecosystem Advantage:

First intelligent vision sensor with integrated AI processing On-sensor machine learning eliminates cloud dependency 12.3MP resolution with ultra-low 100mW power consumption

⚡ III. Advantages of Arducam AI Camera Solution

Arducam’s AI camera solutions are dedicated to solving key parking challenges in urbanization – to maximize asset utilization while providing a driver-centric experience.

AI Model Dataset

Arducam Pre-trained Barcode Code detection dataset

AI Model Example

Hardware Preparation

The following demo is based on Arducam IMX500 Raspberry Pi 5 All-in-one AI Camera Kit:

Product Image	SKU	Resolution	Sensor	Lens Mount	Focus Type	Field of View(DxHxV)	IR Sensitivity	Power Requirements	Operating Temp.
	B0540	12.3MP	SONY IMX500	Stock Lens	Fixed Focs	78±3°(D)×66±3°(H)×52.3±3°(V)	Integral 650nm IR Filter	Max 7.5W	0°C to 50°C

Software Preparation

Export IMX500 AI model compressed package

IMX500 AI Model Package-Parking Space Detection

Deployment

Start the IMX500 Raspberry Pi All-in-One and run the following code to install the environment

sudo apt update && sudo apt full-upgrade
sudo apt install imx500-all imx500-tools
sudo apt install python3-opencv python3-munkres

Use the following command to package the IMX500 model into rpk (note to replace the path example below with the actual path)

imx500-package -i <path to packerOut.zip> -o <output folder>

Download and install picamera2, and run the startup script (note to replace the actual rpk model path)

git clone https://github.com/raspberrypi/picamera2
cd picamera2
pip install -e .  --break-system-packages
cd examples/imx500
python imx500_object_detection_demo.py --model ~/dev_workspace/output/network.rpk --fps 17 --bbox-normalization --ignore-dash-labels --bbox-order xy --labels ~/dev_workspace/parking.txt