Fei Dai

Tack coat is a thin asphalt applied between the existing surface and asphalt overlay during road rehabilitation. Its coverage uniformity is vital in providing adhesive bonding between the two layers in the structure. This paper proposes a novel method for automatic uniformity inspection of tack coat coverage using images collected by drones. In this method, color thresholding is first performed to extract coarse region of the tack coat. Then, the boundaries of the tack coat are detected to refine the tack coat region by masking outliers. Next, the region of interest is processed to extract texture features using gray-level co-occurrence. Finally, the texture features are leveraged to grade the tack coat uniformity level using LightGBM. Experiments were conducted to examine the performance of the method. The resulting accuracy signifies the potential of this method in developing high-efficiency and cost-effective solutions to enhance the current tack coat inspection practice.

In construction, the accurate measurements are important in ensuring the quality of work delivered. Different measuring tools have been developed to help workers conduct accurate measuring. However, they may be subject to manipulation difficulties, such as the need for tap/gesture interaction. This paper proposes a novel eye gaze-aided virtual tape measure framework that provides a hands-free manner for conducting the measurements in construction. This framework consists of three components: data collection for point of interest, sensor calibration, and distance calculation. Its effectiveness is tested by measuring the dimensions of 15 common objects in laboratory and on-site environments and achieves the average absolute and relative errors of 2.4 cm and 4.8%. The absolute errors range from 0.3 to 7.3 cm. A comparison study is conducted to demonstrate its superior performance over iPhone’s Measure application. The results illustrate the feasibility and potential of using the framework to enable measures for smart construction.