Validating the practicality of utilising an image classifier developed using TensorFlow framework in collecting corrugation data from gravel roads

ABSTRACT

Gravel roads management systems (GRMS) are in need of an integrated and cost-effective approach for condition data collection. In order to fulfil this need, this paper validates the practicality of utilising deep learning and image classifiers in collecting corrugation data from gravel roads. The used image classifier in this study was developed using the TensorFlow framework. This classifier has the capability to recognise and classify the corrugation severity on gravel roads into five levels. Furthermore, a pilot study was carried out in Laramie County, Wyoming to validate the applicability of the developed classifier in real practice. Three thousand images of gravel roads were captured from Laramie County gravel roads. Each captured image represents one gravel road section. The corrugation in the tested sections was evaluated by two methods, visual inspection and the developed image classifier. A confusion matrix was developed to determine the achieved accuracy by utilising the gravel roads corrugation image classifier. The confusion matrix showed that the developed image classifier has an 83% accuracy level in the practical field. The achieved accuracy level is considered sufficient for the purpose of GRMS.

KEYWORDS:

• Gravel roads
• deep learning
• machine learning
• TensorFlow
• corrugation
• road management

Acknowledgements

The authors would like to gratefully thank the Mountain Plains Consortium (MPC) for supporting this study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

The authors would like to gratefully thank the Mountain Plains Consortium (MPC) for supporting this study.