Convolutional Neural Network Approach for Automobile Fault Detection Using Workshop Images

Most automobile repair and maintenance apps utilize the rule base and case base reasoning methodologies in their implementations. These two methodologies have their strengths and limitations, some of these limitations can be overcome by the Convolutional Neural Networks (CNN), a specialized subset of deep learning that has excelled in image analysis due to its ability to learn hierarchical representations (Brito, 2023). CNN extracts the pixel value of an image and create a feature map that it uses for processing through learning. In this study, 998 images of frequently occurring vehicle faults were captured from mechanic workshops operated within and by Akwa Ibom state Transport company. These images were subjected to 3 lightweight CNN models after pre-processing. The aim was to detect and classify faults in these damaged parts. Results obtained show that the 3 models demonstrated strong performance across the three key evaluation metrics: accuracy, precision, and recall with an accuracy of 92%, Precision of 91% and Recall of 90%. It is recommended that an integration of case-based reasoning, Fuzzy logic reasoning and CNN be undertaken to improve on the results and a high resolution camera be used to capture the images of the damaged parts for a better input to the CNN model.