Avoiding defective roads is very crucial for driving in an accident-prone country like Bangladesh. Also, in order to use maintenance resources correctly and efficiently, roads need to be continuously monitored. With the help of deep learning, it is possible to solve this problem. Using deep learning, a road defect detection model can precisely detect defects in the roads and alert the concerned authority to repair or save a careless driver from possible danger.
In this research, we have implemented a deep learning-based instance segmentation model with the transfer learning technique. The Mask R-CNN model with ResNet-101-FPN backbone has been trained and tested on our collected dataset with different batch sizes to detect road defects. After that, we evaluated the model using the AP matrix and compared their results. The model produced a satisfactory result as it can detect the defect's shape with a good confidence level and predict the damaged areas with different color annotations.
