Detecting temperature segregation in asphalt pavement construction using infrared imaging and deep learning
Jiachen Shi, Hongren Gong, Cong Lin, Minda Ren, Haimei Liang
Abstract
Temperature segregation (TS) is the uneven distribution of mixture temperature during construction. Considering segregation patterns is essential to detect TS and evaluate construction quality. This paper collected infrared images and classified three segregation categories: interlaced TS, banded TS (BTS), and clustered TS (CTS) based on temperature distribution patterns. Detecting TS using infrared image and deep learning is benefit for improving construction quality, so we labelled the BTS and CTS images to form an original training dataset (OD) for deep learning detection algorithms. We also used two image augmentation approaches to expand OD and obtained two more datasets: dataset augmented without CutMix, which is a classic image augmentation method, and dataset augmented with CutMix (CD). Based on three datasets, we trained You Only Look Once version 5 (YOLOv5) models to detect TS and compared them with Faster R-CNN and YOLOv7. CDM-YOLOv5, YOLOv5 trained on CD, outperformed others, with a mean average precision of 71.5% and an F1 score of 69.7%. We also observed performance gains using the CutMix augmentation. The findings contribute to automating construction quality evaluation and control.