CNN Parking Space Detection Using Point Regression and Line Relations
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Solution Overview
Problem
Current technologies for autonomous parking lack efficient methods for detecting parking spaces, relying heavily on sensors and lacking comprehensive studies on using Deep Convolutional Neural Networks (CNNs) for this purpose, which could lead to financial losses due to inefficient parking practices.
Innovation Solution
A learning method and device utilizing a Convolutional Neural Network (CNN) to perform point regression and detect relationships between points in images, generating estimated probability distributions and relational linear segment information to identify unoccupied parking spaces, allowing for easier and more economical auto-parking systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors are used to detect parking spaces, then detection reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces traditional mechanical/optical sensors with a deep learning-based image processing system. A convolutional neural network (CNN) processes images from the vehicle's camera to detect parking spaces, replacing complex sensor arrays with a software-based solution that achieves comparable or superior detection reliability while reducing hardware complexity
Solution Approach 2:
The system creates a digital representation (segmentation map) of the parking environment from camera images. The CNN generates pixel-level labels that copy the physical parking space structure into a processed image format, enabling detection without direct sensor contact with the parking spaces
2Device complexity
If traditional image processing methods are used, then device complexity is reduced, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent transforms the detection problem from traditional image processing parameters to deep learning parameters. The CNN architecture with multiple convolutional layers, activation functions, and pooling operations changes the processing parameters dynamically, enabling high-precision detection of parking space boundaries, angles, and occupancy status
Solution Approach 2:
The system moves from 2D image processing to 3D feature extraction by incorporating spatial relationships, depth information from multiple image layers, and contextual features from the training data. The CNN processes images in multiple dimensions simultaneously, detecting not only parking space locations but also their geometric properties and relationships with surrounding objects
3Measurement precision
If deep learning models are trained with large datasets, then detection accuracy is improved, but loss of time during training increases
Solution Approach 1:
The system performs preliminary actions by pre-training the CNN on a large dataset of labeled parking images before deployment. The model learns general parking space patterns, vehicle types, and environmental conditions in advance, so that during actual operation it can quickly adapt to new situations with minimal additional training time
Solution Approach 2:
The training process is made dynamic through transfer learning and fine-tuning capabilities. The model can be initially trained on a large dataset for general accuracy, then quickly adapted to specific environments or conditions with smaller datasets, balancing training time investment with detection accuracy requirements for different deployment scenarios
Data Source
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AI summary
A learning method for detecting unoccupied parking spaces by using probability distributions on decision points of the unoccupied parking spaces and relational linear segment information on relationships among the decision points is provided. And the method includes steps of: (a) a learning device performing (i) a process of instructing a first CNN to apply a first CNN regression operation to a parking circumstance image, to thereby calculate each of one or more estimated probability distributions, and (ii) a process of instructing a second CNN to apply a second CNN regression operation to the parking circumstance image, to thereby generate estimated relational linear segment information; and (b) the learning device instructing a loss layer to perform (i) a process training parameters in the first CNN by performing backpropagation using a first loss, and (ii) a process of training of parameters in the second CNN by performing backpropagation using a second loss.