The Effect of Comparison of Images Using the Deep Learning Model CNN Yolov4 Method on Car Detection for Parking Lots

Oct 13, 2025 by thesweetdesigns.com 144 views

The Effect of Comparison of Images Using the Deep Learning Model CNN Yolov4 Method on Car Detection for Parking Lots

Introduction

Parking Lot Congestion: A Growing Concern

Congestion and long queues in the parking lot are often caused by the less optimal process of entering the vehicle. Traditional systems that rely on human labor are vulnerable to errors, such as lack of vigilance of officers or concentration disruption. This encourages research to develop a more efficient and accurate system, one of which is by utilizing artificial intelligence (AI) through the deep learning method.

The Need for Deep Learning in Parking Systems

Deep learning has emerged as a powerful tool in various applications, including image recognition, object detection, and classification. In the context of parking systems, deep learning can be used to develop a more efficient and accurate system for detecting cars in the parking lot. This can help reduce congestion, increase security, and provide a more comfortable parking experience for users.

Background

Convolutional Neural Networks (CNNs) and Object Detection

Convolutional Neural Networks (CNNs) are a type of deep learning model that is particularly well-suited for image recognition and object detection tasks. One of the most popular CNN architectures for object detection is the You Only Look Once (Yolo) model. Yolo is known for its ability to detect objects in real-time, making it an ideal choice for applications such as self-driving cars and surveillance systems.

The Yolo Model: A Brief Overview

The Yolo model is a type of CNN that uses a single neural network to predict the locations and classes of objects in an image. The model consists of several layers, including convolutional layers, pooling layers, and fully connected layers. The output of the model is a set of bounding boxes that contain the predicted locations and classes of objects in the image.

Methodology

Data Collection and Preprocessing

The data used in this study consisted of real cars with variations of colors, angles, and distance. The images were captured using an ESP32-Cam sensor and then preprocessed to remove any noise or artifacts. The preprocessed images were then used to train the Yolo model.

Training the Yolo Model

The Yolo model was trained using a dataset of 10,000 images, each with a size of 250x150 pixels. The model was trained using a batch size of 32 and a learning rate of 0.001. The training process consisted of several stages, including data augmentation, model initialization, and training.

Testing the Yolo Model

The trained Yolo model was then tested using a dataset of 2,000 images, each with a size of 250x150 pixels. The model was tested using a batch size of 32 and a learning rate of 0.001. The testing process consisted of several stages, including data augmentation, model initialization, and testing.

Results

Effect of Image Size on Car Detection Accuracy

The results of this study show that the Yolo model is able to achieve high accuracy in detecting cars, even with variations in the size of the image. The F1 score, which is a metric that measures the average precision and recall, reaches 99.59% for the size of the 250x150 pixel and 225x125 pixels. In the size of the 200x100 pixel image, the F1 score is still relatively high, which is 98.58%.

Comparison of Image Sizes

The results of this study show that the Yolo model is able to maintain high detection accuracy even though the image size is changed. This provides flexibility in the application of a car detection system in the parking lot, because it allows the use of cameras with different resolution without reducing detection performance.

Discussion

The Potential of Deep Learning in Parking Systems

This study proves the great potential of the Deep Learning model, especially Yolo, in increasing the efficiency and accuracy of the parking system. The Yolo model is able to detect cars with high precision, even with variations in the size of the image. This opens opportunities for the development of a more sophisticated and automatic parking system, which can reduce the queue, increase security, and provide a more comfortable parking experience for users.

Future Directions

This study provides a foundation for future research in the development of deep learning-based parking systems. Future studies can focus on improving the accuracy of the Yolo model, developing more sophisticated parking systems, and exploring the use of other deep learning models for parking applications.

Conclusion

The Effect of Comparison of Images Using the Deep Learning Model CNN Yolov4 Method on Car Detection for Parking Lots

This study demonstrates the effectiveness of the Yolo model in detecting cars in the parking lot, even with variations in the size of the image. The results of this study show that the Yolo model is able to achieve high accuracy in detecting cars, even with variations in the size of the image. This provides flexibility in the application of a car detection system in the parking lot, because it allows the use of cameras with different resolution without reducing detection performance.
Frequently Asked Questions (FAQs) About the Effect of Comparison of Images Using the Deep Learning Model CNN Yolov4 Method on Car Detection for Parking Lots

Q: What is the Yolo model and how does it work?

A: The Yolo model is a type of Convolutional Neural Network (CNN) that is particularly well-suited for object detection tasks. It works by using a single neural network to predict the locations and classes of objects in an image. The model consists of several layers, including convolutional layers, pooling layers, and fully connected layers.

Q: What is the purpose of this study?

A: The purpose of this study is to investigate the effect of image size on car detection accuracy using the Yolo model. The study aims to determine whether the Yolo model is able to maintain high detection accuracy even with variations in the size of the image.

Q: What are the benefits of using the Yolo model for car detection?

A: The Yolo model has several benefits for car detection, including high accuracy, fast processing time, and flexibility in image size. These benefits make the Yolo model an ideal choice for applications such as self-driving cars and surveillance systems.

Q: How does the Yolo model handle variations in image size?

A: The Yolo model is able to handle variations in image size by using a technique called data augmentation. Data augmentation involves artificially increasing the size of the training dataset by applying random transformations to the images, such as rotation, scaling, and flipping.

Q: What are the limitations of the Yolo model?

A: The Yolo model has several limitations, including the need for large amounts of training data, the risk of overfitting, and the potential for false positives. These limitations can be mitigated by using techniques such as data augmentation, regularization, and ensemble methods.

Q: How can the Yolo model be used in real-world applications?

A: The Yolo model can be used in a variety of real-world applications, including self-driving cars, surveillance systems, and parking systems. The model can be used to detect objects such as cars, pedestrians, and bicycles, and can be integrated with other sensors and systems to provide a comprehensive view of the environment.

Q: What are the potential applications of the Yolo model in the parking industry?

A: The Yolo model has several potential applications in the parking industry, including automatic parking systems, parking lot monitoring systems, and vehicle tracking systems. The model can be used to detect vehicles and track their movement, providing a more efficient and accurate parking experience for users.

Q: How can the Yolo model be improved?

A: The Yolo model can be improved by using techniques such as data augmentation, regularization, and ensemble methods. Additionally, the model can be fine-tuned for specific applications by adjusting the hyperparameters and training the model on a larger dataset.

Q: What are the future directions for research on the Yolo model?

A: Future research on the Yolo model should focus on improving the accuracy and robustness of the model, as well as exploring new applications and use cases. Additionally, researchers should investigate the use of other deep learning models and techniques for object detection and tracking.

Q: How can the Yolo model be used in conjunction with other sensors and systems?

A: The Yolo model can be used in conjunction with other sensors and systems, such as lidar, radar, and cameras, to provide a comprehensive view of the environment. The model can be integrated with other systems to provide a more accurate and efficient parking experience for users.

Q: What are the potential risks and challenges associated with using the Yolo model?

A: The Yolo model has several potential risks and challenges associated with its use, including the risk of false positives, the risk of overfitting, and the potential for bias in the model. These risks and challenges can be mitigated by using techniques such as data augmentation, regularization, and ensemble methods.