For proper construction site management and plan revisions during construction, it is necessary to understand a construction site’s status in real time. Many vision-based construction site-monitoring methods exist, but current technology has not achieved the accuracy required to robustly recognize objects such as construction equipment, workers, and materials in actual jobsite images. To address this issue, this paper proposes a deep convolutional network-based construction object-detection method to accurately recognize construction equipment. A deep convolutional network can achieve high performance in various visual tasks, but is not easy to be applied in the construction industry where there is not enough publicly available data for training. This problem is solved by transfer learning, which trains a model for the construction industry by transferring the knowledge of models trained in other domains with a large amount of training data. To evaluate the proposed method, a benchmark data set is created for five classes: a dump truck, excavator, loader, concrete mixer truck, and road roller. This benchmark data set includes various shapes and poses for each class to evaluate the generalization performance of the proposed construction equipment detection model. Experimental results show that the proposed method performs remarkably well, achieving 96.33% mean average precision. In the future, the proposed model can be used to infer the context of construction operations for producing managerial information such as progress, productivity, and safety.

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).