An Object Detection Framework and Deep Learning Models Used to Detect the Potholes on the Streets

Potholes are a significant cause of road accidents and car damage. Poor construction and heavy rainfalls result in substantial damage to the roadways. However, detecting potholes manually can be both time-consuming and inaccurate. To solve the problem, new technology has been suggested to detect potholes on muddy and highway routes, thereby preventing vehicle damage and tragedies. This technology employs deep learning systems to identify images and estimate road plainness or potholes. The data collection is practically automated using various imaging devices today. However, fault assessment from collected data is still done manually. Manually classifying and inspecting potholes is costly and time-consuming, slowing down the overall road maintenance process. A comprehensive realtime comparison of advanced deep learning models and object identification frameworks, including YOLOv2, YOLOv3, YOLOv4, YOLOv5, CNN, and SSD-mobilenetv2, for detecting potholes were used in this work. The models were evaluated based on Accuracy, Precision, Recall, and F1-Score. Detecting potholes can also help prevent accidents on roads and highways.