The use of UAVs for monitoring and inspection in the construction industry has garnered considerable attention in recent years due to their potential to enhance safety, efficiency, and accuracy. The development and application of various types of drones and sensors in the construction industry have opened up new data collection and analysis possibilities. This paper provides a thorough examination of the latest developments in the use of UAVs for monitoring and inspection in the construction industry, including a review of the current state of UAVs and an exploration of the types of drones and sensors applied and their applications. It also highlights the technological advancements in this field. However, as with any new technology, there are challenges and limitations that need to be addressed, such as regulatory and legal concerns, technical limitations, data processing challenges, training and expertise, and safety. Finally, we offer insights into potential solutions to these challenges, such as innovative sensors and imaging technologies, integration with other construction technologies, and the use of machine learning and AI for data analysis, which are some of the potential areas for future investigation, and highlight the prospects for drone-based construction inspection.

To reduce the risk of head trauma to workers working in high-risk workplaces such as construction sites, we designed a new automated lightweight end-to-end convolutional neural network to identify whether all people on a construction site are wearing helmets. Firstly, we used GhostNet as the backbone feature extraction network to take advantage of its low running cost and make the model lighter overall while ensuring efficient automatic feature extraction. Secondly, we designed a multi-scale segmentation and feature fusion network (MSFFN) in the feature-processing stage to improve the algorithm’s robustness in detecting objects at different scales. In contrast, the design of the feature fusion network can enrich the diversity of helmet features and improve the accuracy of helmet detection when distance changes, viewpoint changes, and occlusion phenomena occur. Thirdly, we proposed an improved version of the attention mechanism, the lightweight residual convolutional attention network version 2 (LRCA-Netv2). The main idea of the improvement is implemented around the spatial dimension by fusing the combined features along with the horizontal and vertical directions and then weighting them separately. Such an operation allows the establishment of dependencies between the more distant features with improved accuracy compared to the original LRCA-Net. Finally, when tested on the dataset, the proposed lightweight helmet-wearing detection network has a mAP and FPS of 93.5% and 42.

An efficient road damage detection system can reduce the risk of road defects to motorists and road maintenance costs to traffic management authorities, for which a lightweight end-to-end road damage detection network is proposed in this paper, aiming at fast and automatic accurate identification and classification of multiple types of road damage. The proposed technique consists of a backbone network based on a combination of lightweight feature detection modules constituted with a multi-scale feature fusion network, which is more beneficial for target identification and classification at different distances and angles than other studies. An embedded lightweight attention module was also developed that can enhance feature information by assigning weights to multi-scale convolutional kernels to improve detection accuracy with fewer parameters. The proposed model generally has higher performance and fewer parameters than other representative models. According to our practice tests, it can identify many types of road damage based on the images captured by vehicle cameras and meet the real-time detection required when piggybacking on mobile systems.

Detecting damage in bridges can be an arduous task, fraught with challenges stemming from the limitations of the inspection environment and the considerable time and resources required for manual acquisition. Moreover, prevalent damage detection methods rely heavily on pixel-level segmentation, rendering it infeasible to classify and locate different damage types accurately. To address these issues, the present study proposes a novel fully automated concrete bridge damage detection system that harnesses the power of unmanned aerial vehicle (UAV) remote sensing technology. The proposed system employs a Swin Transformer-based backbone network, coupled with a multi-scale attention pyramid network featuring a lightweight residual global attention network (LRGA-Net), culminating in unprecedented breakthroughs in terms of speed and accuracy. Comparative analyses reveal that the proposed system outperforms commonly used target detection models, including the YOLOv5-L and YOLOX-L models. The proposed system’s robustness in visual inspection results in the real world reinforces its efficacy, ushering in a new paradigm for bridge inspection and maintenance. The study findings underscore the potential of UAV-based inspection as a means of bolstering the efficiency and accuracy of bridge damage detection, highlighting its pivotal role in ensuring the safety and longevity of vital infrastructure.

Automated construction site supervision systems are critical for reducing accident risks. We propose a helmet detection system with low-altitude remote sensing by UAVs in this paper to automate the detection of helmet-wearing workers to overcome the limitations of most detection efforts that rely on ground surveillance cameras and improve the efficiency of safety supervision. The proposed system has the following key aspects. (1) We proposed an approach for speedy automatic helmet detection at construction sites regularly leveraging the flexibility and mobility of UAVs. (2) A single-stage high-precision attention-weighted fusion network is proposed, allowing the detection AP of small-sized targets to be enhanced to 88.7%, considerably improving the network’s detection performance for small-sized targets. (3) Our proposed method can accurately categorize helmets based on whether they are worn or not and the type of helmet color, with an mAP of 92.87% and maximum detection accuracy in each category.

On-site management of construction sites has always been a significant problem faced by the construction industry. With the development of UAVs, their use to monitor construction safety and progress will make construction more intelligent. This paper proposes a multi-category target detection system based on UAV low-altitude remote sensing, aiming to solve the problems of relying on fixed-position cameras and a single category of established detection targets when mainstream target detection algorithms are applied to construction supervision. The experimental results show that the proposed method can accurately and efficiently detect 15 types of construction site targets. In terms of performance, the proposed method achieves the highest accuracy in each category compared to other networks, with a mean average precision (mAP) of 82.48%. Additionally, by applying it to the actual construction site, the proposed system is confirmed to have comprehensive detection capability and robustness.