Deep Learning Based Infrared Thermal Image Analysis of Complex Pavement Defect Conditions Considering Seasonal Effect

Abstract

Deep learning techniques underpinned by extensive data sources encompassing complex pavement features have proven effective in early pavement damage detection. With pavement features exhibiting temperature variation, inexpensive infra-red imaging technology in combination with deep learning techniques can detect pavement damages effectively. Previous experiments based on pavement data captured during summer sunny conditions when subjected to SA-ResNet deep learning architecture technique demonstrated 96.47% prediction accuracy. This paper has extended the same deep learning approach to a different dataset comprised of images captured during winter sunny conditions to compare the prediction accuracy, sensitivity and recall score with summer conditions. The results suggest that irrespective of the prevalent weather season, the proposed deep learning algorithm categorises pavement features around 92% accurately (95.18% in summer and 91.67% in winter conditions), suggesting the beneficial replacement of one image type with other. The data captured in sunny conditions during summer and winter show prediction accuracies of DC = 96.47% > MSX = 95.24% > IR-T = 93.83% and DC = 94.14% > MSX = 90.69% > IR-T = 90.173%, respectively. DC images demonstrated a sensitivity of 96.47% and 94.20% for summer and winter conditions, respectively, to demonstrate that reliable categorisation is possible with deep learning techniques irrespective of the weather season. However, summer conditions showing better overall prediction accuracy than winter conditions suggests that inexpensive IR-T imaging cameras with medium resolution levels can still be an economical solution, unlike expensive alternate options, but their usage has to be limited to summer sunny conditions.