Semantic Segmentation for Identifying Road Surface Damages Using Lightweight Encoder-Decoder Network

With the increasing growth of road infrastructure in recent decades, road surface damage is becoming more prevalent. The rapid advance of neural networks and their intelligent technologies can scale up efforts to help deal with this problem. One of the technologies that can be applied in this context is computer vision with semantic segmentation, which can help automatically identify road surface damage. While a naive implementation of semantic segmentation often sacrifices running time and speed performance, in this study, we propose the lightweight encoder-decoder network model to overcome this issue. Numerical experiments show that this method gives us 110 minutes running time and is able to run at 26 fps, which can boost nearly 2× than the baseline model’s running time and speed performance for automated road surface damage identification tasks and can be extended to automatically measure the area of road damage and provide more meaningful information for decision-makers.