Monocular 2D Camera-based Proximity Monitoring for Human-Machine Collision Warning on Construction Sites

Accident of struck-by machines is one of the leading causes of casualties on construction sites. Monitoring workers' proximities to avoid human-machine collisions has aroused great concern in construction safety management. Existing methods are either too laborious and costly to apply extensively, or lacking spatial perception for accurate monitoring. Therefore, this study proposes a novel framework for proximity monitoring using only an ordinary 2D camera to realize real-time human-machine collision warning, which is designed to integrate a monocular 3D object detection model to perceive spatial information from 2D images and a post-processing classification module to identify the proximity as four predefined categories: Dangerous, Potentially Dangerous, Concerned, and Safe. A virtual dataset containing 22000 images with 3D annotations is constructed and publicly released to facilitate the system development and evaluation. Experimental results show that the trained 3D object detection model achieves 75% loose AP within 20 meters. Besides, the implemented system is real-time and camera carrier-independent, achieving an F1 of roughly 0.8 within 50 meters under specified settings for machines of different sizes. This study preliminarily reveals the potential and feasibility of proximity monitoring using only a 2D camera, providing a new promising and economical way for early warning of human-machine collisions

VCVW-3D: A Virtual Construction Vehicles and Workers Dataset with 3D Annotations

Currently, object detection applications in construction are almost based on pure 2D data (both image and annotation are 2D-based), resulting in the developed artificial intelligence (AI) applications only applicable to some scenarios that only require 2D information. However, most advanced applications usually require AI agents to perceive 3D spatial information, which limits the further development of the current computer vision (CV) in construction. The lack of 3D annotated datasets for construction object detection worsens the situation. Therefore, this study creates and releases a virtual dataset with 3D annotations named VCVW-3D, which covers 15 construction scenes and involves ten categories of construction vehicles and workers. The VCVW-3D dataset is characterized by multi-scene, multi-category, multi-randomness, multi-viewpoint, multi-annotation, and binocular vision. Several typical 2D and monocular 3D object detection models are then trained and evaluated on the VCVW-3D dataset to provide a benchmark for subsequent research. The VCVW-3D is expected to bring considerable economic benefits and practical significance by reducing the costs of data construction, prototype development, and exploration of space-awareness applications, thus promoting the development of CV in construction, especially those of 3D applications

Scene restoration from scaffold occlusion using deep learning-based methods

The occlusion issues of computer vision (CV) applications in construction have attracted significant attention, especially those caused by the wide-coverage, crisscrossed, and immovable scaffold. Intuitively, removing the scaffold and restoring the occluded visual information can provide CV agents with clearer site views and thus help them better understand the construction scenes. Therefore, this study proposes a novel two-step method combining pixel-level segmentation and image inpainting for restoring construction scenes from scaffold occlusion. A low-cost data synthesis method based only on unlabeled data is developed to address the shortage dilemma of labeled data. Experiments on the synthesized test data show that the proposed method achieves performances of 92% mean intersection over union (MIoU) for scaffold segmentation and over 82% structural similarity (SSIM) for scene restoration from scaffold occlusion

Performance of supersonic steam ejectors considering the nonequilibrium condensation phenomenon for efficient energy utilisation

Supersonic ejectors are of great interest for various industries as they can improve the quality of the low-grade heat source in an eco-friendly and sustainable way. However, the impact of steam condensation on the supersonic ejector performances is not fully understood and is usually neglected by using the dry gas assumptions. The non-equilibrium condensation occurs during the expansion and mixing process and is tightly coupled with the high turbulence, oblique and expansion waves in supersonic flows. In this paper, we develop a wet steam model based on the computational fluid dynamics to understand the intricate feature of the steam condensation in the supersonic ejector. The numerical results show that the dry gas model exaggerates the expansion characteristics of the primary nozzle by 21.95%, which predicts the Mach number of 2.00 at the nozzle exit compared to 1.64 for the wet steam model. The dry gas model computes the static temperature lower to 196 K, whereas the wet steam model predicts the static temperature should above the triple point due to the phase change process. The liquid fraction can reach 7.2% of the total mass based on the prediction of the wet steam model. The performance analysis indicates that the dry gas model over-estimates a higher entrainment ratio by 11.71% than the wet steam model for the steam ejector