Road Damage Detection Using RetinaNet
Road damage detection is of importance to road maintenance, which typically requires huge amount of manual efforts. In this work, we leverage deep learning models to analyze the road images to detect road damages efficiently. Specifically, we train and test different deep learning methods to identify efficient models with high accuracy. Although two stage detector, applied object classification and bounding box regression on proposed regions, can achieve high accuracy in object detection, it runs slow due to its high computational cost. Instead, we adopt one stage detectors, classification and bounding box regression at a single stage, because they are faster than two stage detectors. In this work, we have trained and tested several one stage models and found a fast model called RetinaNet can detect road damages with relatively high accuracy. A user guide of the source code and predicted examples of trained model have been provided as a markdown document and jupyter notebook, respectively1.
2018 IEEE International Conference on Big Data (Big Data)