Dependence on lighting conditions

Oct 13, 2025 by magicalceylontours.com 84 views

Introduction

In today's fast-paced world, vehicles have become an essential mode of transportation for many people. With the increasing number of vehicles on the road, the risk of vehicle theft has also risen. Vehicle theft is a serious crime that not only affects the owner but also the entire community. The process of returning a stolen vehicle to its legitimate owner can be challenging, especially when the identity of the vehicle has been tampered with or deleted. To combat this issue, a system that integrates with vehicles and makes it difficult to change their identity is designed.

Background

Vehicle ownership verification is a critical process that ensures the legitimacy of vehicle ownership. In Indonesia, the vehicle registration document (BPKB) is the primary document used to verify vehicle ownership. However, the BPKB can be easily manipulated or deleted, making it challenging to verify vehicle ownership. To overcome this problem, a vehicle ownership verification system is proposed that utilizes the Convolutional Neural Network (CNN) algorithm, specifically the Yolov5 model, to authenticate BPKB.

Methodology

The proposed system utilizes a dataset consisting of 203 BPKB cover images and 397 identification section images. The system is equipped with a microcontroller that has Wi-Fi and Buzzer 5V connectivity. The Yolov5 model is used to detect the cover and identify the section of the BPKB. Five different Yolov5 models, namely Yolov5n, Yolov5s, Yolov5M, Yolov5L, and Yolov5X, are tested to determine the most suitable model for the identification process.

Testing and Evaluation

The testing process involves two different scenarios: the effect of external lighting and BPKB placement angle. The lighting scheme includes four conditions: afternoon light, light (1050 lm), dim (200 lm), and dark (0 lm). The placement of BPKB varies from 0˚ to 345˚ with an interval of 15 °, which is carried out in five experiments each. A total of 11,520 experiments are conducted using six original BPKB and six fake BPKB. The results show that the system achieves an average confidence score above 90% for the identification of the cover and above 80% for the identification department.

Results and Discussion

The test results show that the proposed system functions well and meets the intended design objectives. The success of this system not only offers new methods in verifying vehicle ownership but also increases the safety of the practice of increasingly widespread vehicle theft. The system offers an innovative solution that utilizes the latest technology to facilitate the process of identifying vehicles and their owners. With increasing accuracy and efficiency in verification, it is hoped that this system can contribute in reducing the crime of motor vehicle theft and providing a sense of security for the owner.

Conclusion

In conclusion, the proposed system is a vehicle ownership verification system that utilizes the Convolutional Neural Network algorithm, specifically the Yolov5 model, to authenticate BPKB. The system is designed to overcome the problem of vehicle theft by making it difficult to change the identity of the vehicle. The test results show that the system functions well and meets the intended design objectives. The success of this system offers new methods in verifying vehicle ownership and increases the safety of the practice of increasingly widespread vehicle theft.

Future Work

As technology develops, the application of this method can be the first step towards a more sophisticated vehicle security system in the future. The proposed system can be improved by incorporating additional features, such as facial recognition and fingerprint scanning, to enhance the security of the system. Additionally, the system can be integrated with other security systems, such as GPS tracking and alarm systems, to provide a more comprehensive security solution.

References

[1] Yolov5 Model: A deep learning model for object detection that is widely used in various applications, including vehicle detection and recognition.
[2] Convolutional Neural Network (CNN): A type of neural network that is commonly used in image recognition and classification tasks.
[3] Vehicle Registration Document (BPKB): A document that is used to verify vehicle ownership in Indonesia.
[4] Microcontroller: A small computer that is used to control and process data in various applications, including vehicle security systems.

Limitations

The proposed system has several limitations, including:

Limited dataset: The system is trained on a limited dataset of 203 BPKB cover images and 397 identification section images.
Dependence on lighting conditions: The system's performance may be affected by external lighting conditions, such as dim or dark lighting.
Dependence on BPKB placement angle: The system's performance may be affected by the placement angle of the BPKB.

Future Research Directions

Future research directions include:

Improving the accuracy of the system: By incorporating additional features, such as facial recognition and fingerprint scanning, to enhance the security of the system.
Integrating the system with other security systems: To provide a more comprehensive security solution.
Developing a more sophisticated vehicle security system: By incorporating advanced technologies, such as artificial intelligence and machine learning, to provide a more secure and efficient vehicle security system.

Q: What is the purpose of the motor vehicle owner's book identification tool?

A: The motor vehicle owner's book identification tool is designed to verify the ownership of a vehicle by matching the vehicle registration document (BPKB) with the vehicle itself. This tool aims to prevent vehicle theft and ensure the legitimacy of vehicle ownership.

Q: How does the tool work?

A: The tool uses the Convolutional Neural Network (CNN) algorithm, specifically the Yolov5 model, to authenticate BPKB. The system is equipped with a microcontroller that has Wi-Fi and Buzzer 5V connectivity. The tool detects the cover and identifies the section of the BPKB using the Yolov5 model.

Q: What are the benefits of using this tool?

A: The benefits of using this tool include:

Improved security: The tool prevents vehicle theft by verifying the ownership of a vehicle.
Increased accuracy: The tool uses advanced technology to accurately verify the ownership of a vehicle.
Efficient process: The tool streamlines the process of verifying vehicle ownership, reducing the time and effort required.

Q: What are the limitations of the tool?

A: The limitations of the tool include:

Limited dataset: The tool is trained on a limited dataset of 203 BPKB cover images and 397 identification section images.
Dependence on lighting conditions: The tool's performance may be affected by external lighting conditions, such as dim or dark lighting.
Dependence on BPKB placement angle: The tool's performance may be affected by the placement angle of the BPKB.

Q: How can the tool be improved?

A: The tool can be improved by:

Increasing the dataset: Adding more images to the dataset to improve the accuracy of the tool.
Incorporating additional features: Adding features such as facial recognition and fingerprint scanning to enhance the security of the tool.
Integrating with other security systems: Integrating the tool with other security systems, such as GPS tracking and alarm systems, to provide a more comprehensive security solution.

Q: What are the future research directions for the tool?

A: The future research directions for the tool include:

Improving the accuracy of the tool: By incorporating additional features and increasing the dataset.
Developing a more sophisticated vehicle security system: By incorporating advanced technologies, such as artificial intelligence and machine learning, to provide a more secure and efficient vehicle security system.
Integrating the tool with other security systems: To provide a more comprehensive security solution.

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

A: The tool can be used in various real-world applications, including:

Vehicle registration: The tool can be used to verify the ownership of a vehicle during the registration process.
Vehicle inspection: The tool can be used to verify the ownership of a vehicle during inspections.
Vehicle recovery: The tool can be used to verify the ownership of a vehicle during recovery operations.

Q: What are the potential challenges of implementing the tool?

A: The potential challenges of implementing the tool include:

Technical issues: Technical issues, such as connectivity problems or software glitches, may arise during implementation.
Training and education: Users may require training and education to effectively use the tool.
Cost and budget: The cost and budget of implementing the tool may be a challenge.

Q: How can the tool be maintained and updated?

A: The tool can be maintained and updated by:

Regular software updates: Regular software updates can be performed to ensure the tool remains secure and efficient.
Data maintenance: Data maintenance can be performed to ensure the accuracy and reliability of the tool.
User support: User support can be provided to address any technical issues or concerns that may arise.