Aug 2026· International Conference on Industrial IoT, Big Data, and Smart Cities· Vol 14325, pp. 143251O - 143251O-8· 0 citations
Engineering
Abstract
The construction safety of workers in hydraulic construction sites that are crowded and difficult to manage is very serious. When personnel movement is frequent, the status of workers wearing safety helmets is difficult to monitor in real time. The focus of this study is the design of HDS-DETR model which is aimed to improve the safety recognition in hydraulic construction projects. Improvements were achieved by integrating the C2f-HDRAB Module to the RT-DETR model to strengthen the model's ability to detect features, the D-Attention mechanism to improve the model's ability to recognize important features, and SlimNeck architecture was implemented to improve the model's ability to efficiently fuse features. The results of the experiments reflect that the accuracy achieved was 94.1% and 89.6% of the improved model offered by the dedicated dataset in recall, and 94.9% of the mean Average Precision at IoU threshold 0.5, which is a 3.7% increase in the original model. The ablation tests demonstrate the effectiveness of the correction of modules and the proposed design is aimed at the complex nature of hydraulic construction, and provides real-time hard hat wearing monitoring. Safety management of the hydraulic engineering construction project provides support and improves the safety condition recognition in smart water conservancy construction projects.
Safety helmet wearing detection is a crucial component of safety management in construction sites. Traditional detection methods based on manual monitoring are inefficient, while existing deep learning models often suffer from high computational costs and poor detection performance for small or occluded targets. To address these issues, this paper proposes a lightweight detection algorithm named GCW-YOLOv8. Firstly, the Ghost Module is introduced into the backbone network to replace the conventional convolution layers, significantly reducing the number of parameters and floating-point operations (FLOPs) while maintaining feature extraction capability. Secondly, the Coordinate Attention (CA) mechanism is embedded into the neck network to enhance the model's sensitivity to spatial location and channel information, thereby improving the detection accuracy of small targets. Finally, the Wise-IoU (WIoU) loss function is adopted to replace the original CIoU loss, utilizing a dynamic non-monotonic focusing mechanism to optimize the gradient assignment for low-quality samples. Experimental results on the safety helmet dataset show that the proposed algorithm achieves a mean Average Precision (mAP@0.5) of 94.5%, while the inference speed reaches 108 FPS. Compared with the baseline YOLOv8n, our method improves detection accuracy by 1.4% while reducing parameter count by 34.4%, achieving a superior balance between accuracy and efficiency for smart construction site applications.
Zheng Re, Zhisen Ren, Qianru Liu et al.· International Conference on...· 0 citations
Workplaces in construction and industry suffer from a significant number of workplace accidents because of a lack of safety mechanisms like helmets and high-visibility vests. In order to solve this issue, an automatic detection system for detecting the presence of the worker's helmet & safety vest using a deep learning model is developed. You Only Look Once – Neural Architecture Search (YOLO-NAS) algorithm was chosen to be used in the model because of its fast and high-quality detection process. A dataset with images containing healthy workers wearing helmets & safety vests (with some workers appearing in different safety vests) is collected and used as the training set. The resulting model is then saved for future usage in making predictions with new images. Then, this model is connected to Streamlit, which provides a convenient Web-based user interface through which a user can provide an image as an input to the model. The trained neural network is applied to the input image, and all detected objects that correspond to helmet & safety vest are put in bounding boxes on the input image. Moreover, each of the bounding boxes is labeled with the name of the object and confidence score of the detection. Thus, a clear visualization of the safety equipment worn by the worker is provided. Overall, the developed detection system significantly decreases the human intervention needed for visual verification of the safety helmet or vest. The described system is a user-friendly and cost-effective way of evaluating workplace safety compliance.
S. Vijayakumar, Loganathan Nachimuthu, Balasubramaniam C et al.· 2026 International Conferenc...· 0 citations
Road safety and infrastructure maintenance are critical aspects of modern transportation systems to support mobility and protect road users. One major challenge is the presence of nails on roads, which can cause tire damage, traffic disruption, and accidents. This study proposes a road nail detection system using digital image processing constructed proceeding the You Only Look Once (YOLO)v4-tiny algorithm. The model demonstrated promising detection performance, with the loss value decreasing to 0.2876 and the mean Average Precision (mAP) reaching 70% at the 5400th iteration. Although a decline in mAP after this iteration indicated potential overfitting, the model was generally capable of recognizing nail objects within the training dataset. Performance evaluation showed an Average Precision (AP) of 90.87% for the “nail” class, with 394 true positives and 32 false positives, indicating strong detection capability. Additional metrics, including 85% precision, 82% F1-groove, also an average Intersection over Union (IoU) of 67.17%, indicate that the system performs reasonably well. The proposed system has potential applications in preventing tire punctures and improving road safety. Furthermore, this research potentially supports highway patrol officers in monitoring road conditions more efficiently by enabling early detection and rapid removal of hazardous objects such as nails.
M. Delina, Haris Suhendar, Van-Huy Pham et al.· EPJ Web of Conferences· 0 citations
This study suggests an enhanced safety helmet detection method based on YOLOv10 to solve the low detection accuracy of current algorithms for small objects and complicated settings in different situations.
Iqra Aziza Khatoon, Dr. Safia Khanam· International Journal of Dat...· 0 citations
An intelligent safety belt model with self-identification and self-sensing capabilities is developed, which significantly improves safety supervision for work at height and provides proactive protection for personnel performing elevated tasks.
Wending Li, Jian-Lun Lin, Minghui Lin et al.· International Conference on...· 0 citations
Personal protective equipment (PPE) detection is a critical task in construction safety management, yet real-world construction scenes are often affected by occlusion, scale variation, complex backgrounds, and small-object interference. To evaluate the applicability of lightweight object detection for practical site monitoring, this study investigates a six-class PPE detection task in construction scenes based on YOLO11n, covering person, hard hat, vest, safety boots, gloves, and mask. Experiments were conducted on a public PPE dataset containing 1,617 images, which were divided into 1,100 training images, 304 validation images, and 213 test images. The model was trained with an input size of 640 × 640 for 50 epochs and evaluated using Precision, Recall, mAP@0.5, mAP@0.5:0.95, and inference speed. The results show that YOLO11n achieved Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.821, 0.872, 0.846, and 0.566, respectively, on the test set, while maintaining an inference speed of approximately 103 FPS on an RTX 4050 Laptop GPU. Class-wise analysis indicates that person, hard hat, and vest were detected more reliably, whereas mask, gloves, and safety boots remained more challenging because of their small scale, occlusion sensitivity, and ambiguous visual features. Overall, the results demonstrate that lightweight YOLO11n provides a reasonable balance between detection accuracy and real-time performance for six-class PPE monitoring in construction environments, and it can serve as an effective baseline for future improvement in small-object enhancement and deployment-oriented optimization.
Yin Jian, D. Ying, Ying Sim et al.· Journal of Intelligent Decis...· 0 citations