Automated pavement distress detection systems have become increasingly sought after by road agencies to increase 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 study addresses this by practically analysing 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 hyperparameter case studies to bolster practical model instrumentation and implementation. The tests achieve adequate distress detection performance and provide an understanding of how changing aspects of the workflow influence the actual engineering application, thus taking another step towards low-cost automation of aspects of the pavement management system.