Optimization and sensitivity analysis of existing deep learning models for pavement surface monitoring using low-quality images

Automated pavement distress detection systems have become increasingly sought after by road agencies to in crease the efficiency of field surveys and reduce the likelihood of insufficient road condition data. However, many modern approaches are developed without practical testing using real-world scenarios. This paper ad dresses this by practically analyzing Deep Learning models to detect pavement distresses using French Secondary road surface images, given the issues of limited available road condition data in those networks. The study specifically explores several experimental and sensitivity-testing strategies using augmentation and hyper- parameter case studies to bolster practical mode…