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Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Construction progress monitoring (CPM) is considered a difficult and tedious task in construction projects, which focuses on identifying discrepancies between the as-built product and the as-planned design. Computer vision (CV) technology is applied to automate the CPM process. However, the synergy between the CV and CPM in literature and industry practice is lacking. This study aims to fulfil this research gap.

A Delphi qualitative approach was used in this study by conducting two interview rounds. The collected data was analysed using manual content analysis.

This study identified seven stages of CPM; data acquisition, information retrieval, verification, progress estimation and comparison, visualisation of the results and schedule updating. Factors such as higher accuracy in data, less labourious process, efficiency and near real-time access are some of the significant enablers in instigating CV for CPM. Major challenges identified were occlusions and lighting issues in the site images and lack of support from the management. The challenges can be easily overcome by implementing suitable strategies such as familiarisation of the workforce with CV technology and application of CV research for the construction industry to grow with the technology in line with other industries.

This study addresses the gap pertaining to the synergy between the CV in CPM literature and the industry practice. This research contributes by enabling the construction personnel to identify the shortcomings and the opportunities to apply automated technologies concerning each stage in the progress monitoring process.

Images taken from construction site interiors often suffer from low illumination and poor natural colors, which restrict their application for high-level site management purposes. The state-of-the-art low-light image enhancement method provides promising image enhancement results. However, they generally require a longer execution time to complete the enhancement. This study aims to develop a refined image enhancement approach to improve execution efficiency and performance accuracy.

To develop the refined illumination enhancement algorithm named enhanced illumination quality (EIQ), a quadratic expression was first added to the initial illumination map. Subsequently, an adjusted weight matrix was added to improve the smoothness of the illumination map. A coordinated descent optimization algorithm was then applied to minimize the processing time. Gamma correction was also applied to further enhance the illumination map. Finally, a frame comparing and averaging method was used to identify interior site progress.

The proposed refined approach took around 4.36–4.52 s to achieve the expected results while outperforming the current low-light image enhancement method. EIQ demonstrated a lower lightness-order error and provided higher object resolution in enhanced images. EIQ also has a higher structural similarity index and peak-signal-to-noise ratio, which indicated better image reconstruction performance.

The proposed approach provides an alternative to shorten the execution time, improve equalization of the illumination map and provide a better image reconstruction. The approach could be applied to low-light video enhancement tasks and other dark or poor jobsite images for object detection processes.

To be able to achieve the uptake and usage of green buildings requires various actors within the construction value chain to be engaged. Despite its global uptake, green building construction is still at its nascent stage in Ghana. Most studies in sub-Saharan Africa point to the lack of knowledge as one of the mitigating factors against its development. However, there is a dearth of studies assessing the level of knowledge of stakeholders. The terms “knowledge” and “awareness” of green building construction are often used interchangeably in the Ghanaian Construction Industry (GCI). This study seeks to unearth the level of knowledge of stakeholders on green building construction through a comparative analysis of construction professionals and demand-side operators.

A structured questionnaire was issued to professionals in the various recognised bodies in the construction industry and public and private institutions in Ghana. Frequency, Kolmogorov–Smirnov test, median statistics and Mann–Whitney U-Test were used to rank and analyse the level of knowledge of stakeholders.

Construction professionals were more aware of green building construction than the demand-side operators. It was further identified that only a few of these stakeholders had hands-on experience as the majority of them have gained their awareness through research studies. Based on the findings of the study, it was revealed that the concept of green building construction is more abstract to stakeholders than practical despite their positive attitude towards its adoption.

Contextually, the study has aided in showing the level of knowledge of stakeholders on green building construction. The findings of the study aside from it aiding policymakers have also helped in identifying the perceptions and attitudes of stakeholders, their strengths and weakness in green building construction. It is recommended that due to the differences in socio-political structures and construction methods, a clear definition of green building based on the availability of resources in the GCI will encourage its adoption.

The study used two stakeholder groupings in the GCI as the unit of analysis. This enabled insightful discoveries into the knowledge-attitude gap of Ghanaian stakeholders that are driving the adoption of green building.