Integrating deep learning and computer vision in car defect detection is driven by technological advancements, the need for quality control, cost savings, increased accuracy, and the ability to detect defects in real time. AutoScope provides an efficient and reliable means of identifying patterns and anomalies in large amounts of data, leading to improved accuracy and reduced time and costs associated with rectifying defects.
In this project, a deep learning model based on ResNet50 architecture was used for defect detection in cars. The model was trained on a large dataset and achieved an accuracy of 91.7%. The back-end interface for this system was built using FastAPI and the front-end was with ReactJS. This framework allows for real-time defect detection in cars, with the results presented in a user-friendly interface. The high testing accuracy of the model and the use of FastAPI ensures a reliable and efficient system for detecting defects in vehicles.
Collecting and preprocessing the dataset was quite a challenging part. Ensuring that the training data is adequate and unbiased. Integrating the ResNet50 model with the front-end interface.
The implementation of a deep learning-based defect detection system for cars has resulted in several notable accomplishments, including an accuracy of 91.7%, a user-friendly interface using FastAPI, the integration of cutting-edge technologies, improved efficiency, and a contribution to quality control. These achievements demonstrate the effectiveness of deep learning and computer vision in real-world applications and their potential to have a positive impact on the industry and society. The high accuracy, user-friendly interface, and efficient processing make this system a valuable tool for improving the quality control of cars.
Further implementation of the deep learning model can include ensemble learning, and real-time detection, expanding the scope of defects covered, enhancing the user interface, and integrating with existing systems. These improvements can lead to increased accuracy, a more comprehensive solution, and a more streamlined workflow. By continuously advancing the system, it can provide even greater value to the industry and society.