Search Results (52)

In factory workshops, wearing safety helmets is vital for worker safety. However, current deep learning-based detection methods are often hindered by large model parameters and high computational demands, limiting their deployment in resource-constrained settings. This article introduces YOLO-AFL, a novel lightweight model designed to solve these problems. The algorithm introduces several key optimizations to improve performance without increasing computational load. Firstly, the K-Means++ algorithm is applied during the anchor box preprocessing stage, along with a new distance metric (1 − AIoU), which enhances anchor box size estimation and boosts performance without additional overhead. Secondly, by introducing a lightweight PConv operation into the C3 module, the complexity of the model is significantly reduced. Finally, a dual attention network (LDA-GC) is designed to compensate for any accuracy loss caused by the model’s simplifications. Experimental results on a custom dataset show that the proposed algorithm achieves an mAP50 of 94.1%. Compared to the baseline model, it reduces the number of parameters by 19.1% and decreases computational complexity by 16.9%, demonstrating its superior performance and efficiency in safety helmet wearing detection. Full article

In recent years, due to the high number of unsafe factors in the construction industry, its death rate and injury rate have become the focus of research in the field of target detection. In the complex environment of the construction site, workers often forget to wear helmets or fail to wear them properly, causing hidden dangers for the safety of workers. Therefore, this paper proposes an application-oriented improvement strategy for helmet detection based on the YOLOv8 architecture (EC-YOLOv8), which integrates the ECA attention mechanism and content-sensing recombination feature operator into the YOLOv8 network, so as to further improve detection accuracy and detection time, and better meet the actual application requirements of construction sites. Enhanced Intersection over Union Loss (EIoU Loss) is introduced to improve the network model’s ability to evaluate the difference between the predicted boundary box and the real boundary box. The experimental results show that the EC-YOLOv8 algorithm proposed in this paper achieves 95.7% accuracy on the data set SHWD, which improves the detection accuracy of the helmet target. Full article

Background: A non-penal bicycle helmet mandate was implemented in the Republic of Korea on 28 September 2018, yet post-implementation epidemiologic evidence remains limited. This study evaluated changes in helmet wearing and injury outcomes before and after the mandate and identified factors associated with helmet non-use. Methods: We conducted a retrospective observational study using Korea Trauma Data Bank records from January 2017 to December 2022. After exclusions, 7653 bicycle-injured patients were identified, and 1:1 optimal propensity score matching (caliper 0.10) produced 4798 patients (2399 pairs). Outcomes included helmet use, physiological status (AVPU scale and Revised Trauma Score), Injury Severity Score (ISS), Abbreviated Injury Scale by body region, and mortality. Monthly helmet use trends were analyzed using a regression tree to detect data-driven breakpoints and segmented logistic regression at the legal implementation point (22 months) and the regression-tree-identified split (19.5 months). Multivariable logistic regression identified factors independently associated with helmet non-use. Results: Helmet use increased modestly from 17.4% pre-mandate to 21.9% post-mandate (p < 0.001). Interrupted time-series analyses showed no immediate level or slope change at either breakpoint, suggesting gradual uptake. ISS values were higher post-mandate, with ISS > 15 increasing from 21.5% to 24.4% (p = 0.02). Total mortality rose from 3.9% to 5.3% (p = 0.02). Helmet non-use was independently associated with age 80–89 years, female sex, non-Korean nationality, and residence in Jeollabuk-do. Conclusions: The mandate was associated with a modest increase in helmet wearing without an abrupt behavioral shift. These findings suggest that legislation without enforcement may have limited impact as a stand-alone measure and should be complemented by targeted education, visitor-focused communication, and region-specific safety measures. Full article

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

The proper wearing of safety helmets is critical for worker safety in high-risk construction environments, with the fastening of the chin strap serving as a key indicator of correct usage. However, existing detection methods primarily focus on identifying helmet presence, neglecting the crucial assessment of chin strap compliance. This paper proposes an intelligent detection approach that integrates YOLOv8 object detection, instance segmentation, and skin tone recognition to evaluate chin strap wearing status. The system first employs YOLOv8 to detect workers and helmets, filtering out non-wearers before performing facial and neck region segmentation, thereby concentrating computational resources on compliance verification. To address challenges in distinguishing chin straps from similar skin tones under complex lighting conditions, the method incorporates illumination compensation and YCbCr-based skin segmentation. Finally, strap status is determined through morphological operations and contour analysis, with visual annotation of the detection results. This study utilizes a dataset comprising 2000 safety helmet images, which was partitioned into training, validation, and test sets in an 8:1:1 ratio for model training and evaluation. The experimental results demonstrate that the proposed method achieves an accuracy of 96% in detecting chin strap status, exhibits robust performance across diverse construction site conditions, and holds significant practical value and application potential. Full article

Safety on construction sites is an essential yet challenging issue due to the inherently hazardous nature of these sites. Workers are expected to wear Personal Protective Equipment (PPE), such as helmets, vests, and safety glasses, to prevent or minimize their exposure to injuries. However, ensuring compliance remains difficult, particularly in large or complex sites, which require a time-consuming and usually error-prone manual inspection process. The research proposes an automated PPE detection system utilizing the deep learning model YOLO11, which is trained on the CHVG dataset, to identify in real-time whether workers are adequately equipped with the necessary gear. The proposed PPE-EYE method, using YOLO11x, achieved a mAP50 of 96.9% and an inference time of 7.3 ms, which is sufficient for real-time PPE detection systems, in contrast to previous approaches involving the same dataset, which required 170 ms. The model achieved these results by employing data augmentation and fine-tuning. The proposed solution provides continuous monitoring with reduced human oversight and ensures timely alerts if non-compliance is detected, allowing the site manager to act promptly. It further enhances the effectiveness and reliability of safety inspections, overall site safety, and reduces accidents, ensuring consistency in follow-through of safety procedures to create a safer and more productive working environment for all involved in construction activities. Full article

Object detection in complex scenarios such as construction sites, electric power operations, and resource exploration often suffers from low accuracy and frequent missed or false detections. To address these challenges, this study proposes a modified You Only Look Once version 8 nano (YOLOv8n)-based algorithm, termed YOLOv8n-ASA, for safety-helmet-wearing detection. The proposed method introduces structural asymmetry into the network to enhance feature representation and detection robustness. Specifically, an Adaptive Kernel Convolution (AKConv) module is incorporated into the backbone, in which asymmetric kernels are used to better capture features of irregularly shaped objects. The Simple Attention Module (SimAM) further sharpens the focus on critical regions, while the Asymptotic Feature Pyramid Network (AFPN) replaces the symmetric top–down fusion pathway of the traditional FPN with a progressive and asymmetric feature integration strategy. These asymmetric designs mitigate semantic gaps between non-adjacent layers and enable more effective multi-scale fusion. Extensive experiments demonstrate that YOLOv8n-ASA achieves superior accuracy and robustness compared to several benchmarks, validating its effectiveness for safety-helmet-wearing detection in complex real-world scenarios. Full article

Construction sites are complex environments, and unsafe behaviors by workers, such as not wearing safety helmets or reflective vests, can easily lead to accidents. When using target detection technology to detect unsafe behaviors, the results are often unsatisfactory due to the complexity of the background and the small size of the targets. This paper proposes an unsafe behavior detection algorithm based on dual adaptive feature fusion. The algorithm is based on YOLOv5, introducing a front-end adaptive feature fusion module (FE-AFFM) at the head of the backbone network for deep data processing, improving the model’s feature extraction capability in complex backgrounds. Simultaneously, a back-end adaptive feature fusion module (BE-AFFM) is introduced at the tail of the network to strengthen feature fusion. In the experimental verification phase, this paper selects a self-made laboratory dataset and verifies the effectiveness of the improved algorithm through ablation experiments, algorithm comparisons, and heatmap analysis. The average accuracy of the improved algorithm is 3.6% higher than the baseline model, and the detection effect on small targets is significantly improved, meeting the actual needs of construction sites. This paper also selects the publicly available dataset SHWD for algorithm comparison experiments. The results show that the improved algorithm still has a significant advantage over mainstream algorithms, verifying the generalization ability of the improved model. Full article

Although computer vision methods have advanced in construction helmet detection in recent years, their performance heavily depends on large-scale, class-balanced, and diverse annotated datasets. To address the high cost and labor-intensive nature of traditional data collection and annotation, this study introduces a novel helmet detection dataset named AIGC-HWD (Artificial Intelligence-Generated Content–Helmet Wearing Detection), automatically generated using generative AI tools. The dataset contains five categories of labels, supporting both helmet-wearing detection and color classification tasks. We evaluate the standalone performance of AIGC-HWD, as well as its augmentation effect when combined with the real-world dataset GDUT-HWD, using multiple algorithms, including YOLO v8, YOLO v10, YOLO 11, YOLO v11-MobileNet v4, YOLO v13, Faster R-CNN, and RT-DETR. Experimental results show that models trained solely on AIGC-generated images can achieve mAP@50 scores exceeding 0.7 and 0.8 on real-world images in two separate tests, demonstrating a certain level of generalization. When used for data augmentation alongside real-world images, the performance improves to varying degrees—by approximately 1% on the YOLO series, and by over 10% on the two-stage algorithm Faster R-CNN. These findings validate the potential of generative AI images for safety monitoring in construction scenarios and provide new insights into the integration of synthetic and real-world data. Full article

On construction sites, it is crucial and and in most cases mandatory to wear safety equipment such as helmets, safety shoes, vests, and belts. The most important of these is the helmet, as it protects against head injuries and can also serve as a marker for detecting and tracking workers, since a helmet is typically visible to cameras on construction sites. Checking helmet usage, however, is a labor-intensive and time-consuming process. A lot of work has been conducted on detecting and tracking people. Some studies have involved hardware-based systems that require batteries and are often perceived as intrusive by workers, while others have focused on vision-based methods. The aim of this work is not only to detect workers and helmets, but also to identify workers through labeled helmets using symbol detection methods. Person and helmet detection tasks were handled by training existing datasets and gained accurate results. For symbol detection, 14 different shapes were selected and put on helmets in a triple format side by side. A total of 11,243 images have been annotated. YOLOv5 and YOLOv8 were used to train the dataset and obtain models. The results show that both methods achieved high precision and recall. However, YOLOv5 slightly outperformed YOLOv8 in real-time identification tests, correctly detecting the helmet symbols. A testing dataset containing different distances was generated in order to measure accuracy by distance. According to the results, accurate identification was achieved at distances of up to 10 meters. Also, a location-based symbol-ordering algorithm is proposed. Since symbol detection does not follow any order and works with confidence values in the inference mode, a left to right approach is followed. Full article

Safety helmets and gloves are indispensable personal protective equipment in power grid operation environments. Traditional detection methods for safety helmets and gloves suffer from reduced accuracy due to factors such as dense personnel presence, varying lighting conditions, occlusions, and diverse postures. To enhance the detection performance of safety helmets and gloves in power grid operation environments, this paper proposes a novel algorithm, YOLO-DCRCF, based on YOLO11 for detecting the wearing of safety helmets and gloves in such settings. By integrating Deformable Convolutional Network version 2 (DCNv2), the algorithm enhances the network’s capability to model geometric transformations. Additionally, a recalibration feature pyramid (RCF) network is innovatively designed to strengthen the interaction between shallow and deep features, enabling the network to capture multi-scale information of the target. Experimental results show that the proposed YOLO-DCRCF model achieved mAP50 scores of 92.7% on the Safety Helmet Wearing Dataset (SHWD) and 79.6% on the Safety Helmet and Gloves Wearing Dataset (SHAGWD), surpassing the baseline YOLOv11 model by 1.1% and 2.7%, respectively. These results meet the real-time safety monitoring requirements of power grid operation sites. Full article

The environment of the power operation site is complex and changeable, and the accurate identification of the wearing status of workers’ safety helmets is significant to ensure personal safety and the stable operation of the power system. Existing research suffers from high rates of missed detections and limited ability to discriminate fine-grained states, especially the identification of “wrongly wearing” states. Therefore, this paper proposes an intelligent identification method of safety helmet status for power workers based on a dual-path attention network. We embed the convolutional block attention module (CBAM) in the two paths of the backbone and neck layers of YOLOv5 and enhance the feature focusing ability of the key areas of the helmet through the channel-spatial attention coordination, so as to suppress the interference of complex background. In addition, a special dataset covering power scenarios is constructed, including fine-grained state annotation under various lighting, different poses, and occlusion conditions to improve the generalization of the model. Finally, the proposed method is applied to the images of the electric power operation site for experimental verification. The experimental results show that the proposed YOLO-CBAM achieves an outstanding mean average precision of 98.81% for identifying all helmet states, providing reliable technical support for intelligent safety monitoring. Full article

With the rapid advancement of industrial intelligence, ensuring occupational safety has become an increasingly critical concern. Among the essential personal protective equipment (PPE), safety helmets play a vital role in preventing head injuries. There is a growing demand for real-time detection of helmet chinstrap wearing status during industrial operations. However, existing detection methods often encounter limitations such as user discomfort or potential privacy invasion. To overcome these challenges, this study proposes a non-intrusive approach for detecting the wearing state of helmet chinstraps, inspired by the mechanosensory hair arrays found on spider legs. The proposed method utilizes multiple MEMS inertial sensors to emulate the sensory functionality of spider leg hairs, thereby enabling efficient acquisition and analysis of helmet wearing states. Unlike conventional vibration-based detection techniques, posture signals reflect spatial structural characteristics; however, their integration from multiple sensors introduces increased signal complexity and background noise. To address this issue, an improved adaptive convolutional neural network (ICNN) integrated with a long short-term memory (LSTM) network is employed to classify the tightness levels of the helmet chinstrap using both single-sensor and multi-sensor data. Experimental validation was conducted based on data collected from 20 participants performing wall-climbing robot operation tasks. The results demonstrate that the proposed method achieves a high recognition accuracy of 96%. This research offers a practical, privacy-preserving, and highly effective solution for helmet-wearing status monitoring in industrial environments. Full article

This work addresses the high computational cost and excessive parameter count associated with existing helmet-wearing detection models in complex construction scenarios. This paper proposes a lightweight helmet detection model, LSH-YOLO (Lightweight Safety Helmet) based on improvements to YOLOv8. First, the KernelWarehouse (KW) dynamic convolution is introduced to replace the standard convolution in the backbone and bottleneck structures. KW dynamically adjusts convolution kernels based on input features, thereby enhancing feature extraction and reducing redundant computation. Based on this, an improved C2f-KW module is proposed to further strengthen feature representation and lower computational complexity. Second, a lightweight detection head, SCDH (Shared Convolutional Detection Head), is designed to replace the original YOLOv8 Detect head. This modification maintains detection accuracy while further reducing both computational cost and parameter count. Finally, the Wise-IoU loss function is introduced to further enhance detection accuracy. Experimental results show that LSH-YOLO increases mAP50 by 0.6%, reaching 92.9%, while reducing computational cost by 63% and parameter count by 19%. Compared to YOLOv8n, LSH-YOLO demonstrates clear advantages in computational efficiency and detection performance, significantly lowering hardware resource requirements. These improvements make the model highly suitable for deployment in resource-constrained environments for real-time intelligent monitoring, thereby advancing the fields of industrial edge computing and intelligent safety surveillance. Full article

The underground coal mine environment is complex and dynamic, making the application of visual algorithms for object detection a crucial component of underground safety management as well as a key factor in ensuring the safe operation of workers. We look at this in the context of helmet-wearing detection in underground mines, where over 25% of the targets are small objects. To address challenges such as the lack of effective samples for unworn helmets, significant background interference, and the difficulty of detecting small helmet targets, this paper proposes a novel underground helmet-wearing detection algorithm that combines dynamic background awareness with a limited number of valid samples to improve accuracy for underground workers. The algorithm begins by analyzing the distribution of visual surveillance data and spatial biases in underground environments. By using data augmentation techniques, it then effectively expands the number of training samples by introducing positive and negative samples for helmet-wearing detection from ordinary scenes. Thereafter, based on YOLOv10, the algorithm incorporates a background awareness module with region masks to reduce the adverse effects of complex underground backgrounds on helmet-wearing detection. Specifically, it adds a convolution and attention fusion module in the detection head to enhance the model’s perception of small helmet-wearing objects by enlarging the detection receptive field. By analyzing the aspect ratio distribution of helmet wearing data, the algorithm improves the aspect ratio constraints in the loss function, further enhancing detection accuracy. Consequently, it achieves precise detection of helmet-wearing in underground coal mines. Experimental results demonstrate that the proposed algorithm can detect small helmet-wearing objects in complex underground scenes, with a 14% reduction in background false detection rates, and thereby achieving accuracy, recall, and average precision rates of 94.4%, 89%, and 95.4%, respectively. Compared to other mainstream object detection algorithms, the proposed algorithm shows improvements in detection accuracy of 6.7%, 5.1%, and 11.8% over YOLOv9, YOLOv10, and RT-DETR, respectively. The algorithm proposed in this paper can be applied to real-time helmet-wearing detection in underground coal mine scenes, providing safety alerts for standardized worker operations and enhancing the level of underground security intelligence. Full article