INCL: An Effective Deep Learning Model to Identify Road Potholes by Using Image Based Neural Classification Logic

This paper presents a deep learning-based approach for detecting road potholes using image-based neural classification logic. The methodology involves collecting road images from publicly available datasets, particularly from Kaggle, and augmenting the dataset to improve model performance. Preprocessing steps such as resizing, standardizing, and annotating the images were employed to ensure consistency. A pre-trained Faster R-CNN model was fine-tuned for pothole detection, using convolutional neural networks (CNNs) to extract features and classify the road images into pothole and non-pothole categories. The model was trained using transfer learning and tested under varying road and environmental conditions, including day, night, dry, and wet scenarios. The model achieved an accuracy of 96.5% in dry daylight conditions, with a slightly lower performance of 85.4% during wet nighttime conditions. Additionally, precision and recall were evaluated, with the model achieving a precision of 95.3% and a recall of 97.6% for daytime conditions. The system was implemented in a real-time environment, allowing for real-time pothole detection and reporting through a mobile or web application. Future work will aim to improve performance under challenging conditions and enhance real-time deployment efficiency.