A vision-based method for automatic tracking of construction machines at nighttime based on deep learning illumination enhancement

Abstract

Nighttime construction has been widely conducted in many construction scenarios, but it is also much riskier due to low lighting conditions and fatiguing environments. Therefore, this study proposes a vision-based method specifically for automatic tracking of construction machines at nighttime by integrating the deep learning illumination enhancement. Five main modules are involved in the proposed method, including illumination enhancement, machine detection, Kalman filter tracking, machine association, and linear assignment. Then, a testing experiment based on nine nighttime videos is conducted to evaluate the tracking performance using this approach. The results show that the method developed in this study achieved 95.1% in MOTA and 75.9% in MTOP. Compared with the baseline method SORT, the proposed method has improved the tracking robustness of 21.7% in nighttime construction scenarios. The proposed methodology can also be used to help accomplish automated surveillance tasks in nighttime construction to improve the productivity and safety performance.