Pothole detection in bituminous road using CNN with transfer learning

Abstract

Road surfaces are highly affected by climatic changes which caused potholes and cracks. Maintenance of the road is a need-of-the-hour process for preventing the physical damage caused for vehicles. The important process in road maintenance is the detection of potholes and cracks. Automatic detection of potholes in bituminous roads is a tedious task. This paper proposed the detection of potholes using transfer learning and convolution neural networks. The results are promising, and The suggested method can provide valuable information that can be used for various ITS services. One such service is alerting drivers about potholes, allowing them to be more cautious while driving. Additionally, this information can be utilized to assess the initial maintenance needs of a road management system and promptly address any repairs or maintenance required. The achieved results through the proposed method are compared with the state-of-the-art detection algorithms like Transfer Learning + Recurrent neural network, Transfer Learning + Generated adversarial network. In that, the result obtained through the proposed method (Transfer Learning + Convolutional neural networks achieves 96 % of accuracy.