Development of a Multi-Distress Detection System for Asphalt Pavements: Transfer Learning-Based Approach
Naga Siva Pavani Peraka, Krishna Prapoorna Biligiri, Satyanarayana N. Kalidindi
Abstract
The major objective of this research was to develop a multi-distress detection system (MDDS) that is competent in detecting various asphalt pavement functional distresses simultaneously from video images using appropriate artificial intelligence techniques. You Only Look Once Version 4 (YOLOv4), a state-of-the-art objection detection architecture incorporated with transfer learning-based approach was utilized to quantify multiple severity-based distresses obtained from actual pavement condition images. Eighteen distress classes were defined consisting of three levels of severity pertinent to cracking, potholes, and patch deterioration. The customized MDDS algorithm was trained and tested on 1,518 images retrieved from three different datasets. During training, MDDS attained an average loss of 1.5123, and the validation mean average precision was reported to be 87.44% after 7,900 iterations. During the training process, the customized architecture transformed the training images and segmented them into two million images that potentially enhanced the probability of prediction even when the images are spatially transformed. The model detected multiple distresses in the pavement video clip at an average rate of 6.7 frames per second, which makes it suitable for real-time distress detection. It is envisioned that the novel real-time MDDS tested on diverse datasets could be used by roadway agencies to identify and quantify severity-based distress classes during the monitoring process itself, which ultimately reduces the time between data analysis, pavement forensic evaluation, and decision making on maintenance interventions.