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High-resolution image transmission is required in safety helmet detection problems in the construction industry, which makes it difficult for existing image detection methods to achieve high-speed detection. To overcome this problem, a novel super-resolution (SR) reconstruction module is designed to improve the resolution of images before the detection module. In the super-resolution reconstruction module, the multichannel attention mechanism module is used to improve the breadth of feature capture. Furthermore, a novel CSP (Cross Stage Partial) module of YOLO (You Only Look Once) v5 is presented to reduce information loss and gradient confusion. Experiments are performed to validate the proposed algorithm. The PSNR (peak signal-to-noise ratio) of the proposed module is 29.420, and the SSIM (structural similarity) reaches 0.855. These results show that the proposed model works well for safety helmet detection in construction industries.

In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management. This article presents an enhanced algorithm based on YOLOv11n, referred to as GCB-YOLOv11. The proposed improvements are realized through three key aspects: Firstly, the traditional convolution is replaced with GSConv, which significantly enhances feature extraction capabilities while simultaneously reducing computational costs. Secondly, a novel C3K2_FE module was designed that integrates Faster_block and ECA attention mechanisms. This design aims to improve detection accuracy while also accelerating detection speed. Finally, the introduction of the Bi FPN mechanism in the Neck section optimizes the efficiency of multi-scale feature fusion and addresses issues related to feature loss and redundancy. The experimental results demonstrate that GCB-YOLOv11 exhibits strong performance on the dataset concerning mine personnel and safety helmets, achieving a mean average precision of 93.6%. Additionally, the frames per second reached 90.3 f·s−1, representing increases of 3.3% and 9.4%, respectively, compared to the baseline model. In addition, when compared to models such as YOLOv5s, YOLOv8s, YOLOv3 Tiny, Fast R-CNN, and RT-DETR, GCB-YOLOv11 demonstrates superior performance in both detection accuracy and model complexity. This highlights its advantages in mining environments and offers a viable technical solution for enhancing the safety of mine personnel.

Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.

Existing safety helmet detection algorithms face challenges such as insufficient feature extraction for small targets, poor adaptation to multi-scale targets and limited localization accuracy in architectural scenes. To solve the above problems, an improved YOLOv8 model is proposed in this paper. First, the GC block with integrated CBAM attention mechanism is introduced into the C2f module of the backbone network to significantly enhance the focusing ability in critical regions. Second, Ghost-Bottleneck modules with different scales are introduced into the neck network to extract features with different scales and provide rich feature information for the model; it significantly enhances the model’s ability to detect helmet targets with different sizes. Finally, the Wise-IoU loss function is introduced and an adaptive scale-aware mechanism is added to improve the target localization accuracy and robustness of the model in complex scenes. The experimental results on the SWHD dataset show that the improved model achieves 90.4% detection accuracy, which is 4.3% higher than the original model, and 6.2% higher than the mAP, showing significant advantages in complex scenes.

Worker safety at construction sites is a growing concern for many construction industries. Wearing safety helmets can reduce injuries to workers at construction sites, but due to various reasons, safety helmets are not always worn properly. Hence, a computer vision-based automatic safety helmet detection system is extremely important. Many researchers have developed machine and deep learning-based helmet detection systems, but few have focused on helmet detection at construction sites. This paper presents a You Only Look Once (YOLO)-based real-time computer vision-based automatic safety helmet detection system at a construction site. YOLO architecture is high-speed and can process 45 frames per second, making YOLO-based architectures feasible to use in real-time safety helmet detection. A benchmark dataset containing 5000 images of hard hats was used in this study, which was further divided in a ratio of 60:20:20 (%) for training, testing, and validation, respectively. The experimental results showed that the YOLOv5x architecture achieved the best mean average precision (mAP) of 92.44%, thereby showing excellent results in detecting safety helmets even in low-light conditions.

Aiming at real-time monitoring of whether the construction site workers wear helmets correctly according to regulations, we present an improved model which based on the fifth You Only Look Once (YOLOv5) target detection algorithm for helmet wearing detection. Firstly, the clustering algorithm of the network model in the YOLOv5 is optimized to design size of the bounding box. Second, by inducing multi-scale image input to adapt to different image sizes, which enhance the generalization ability of the model. Finally, Complete Intersection over Union (CIoU) instead of original Generalized Intersection over Union (GIoU). CIoU_Loss can entirely consider the distance which are from center point to the length-width ratio between prediction box and ground truth box. The result of final experiment demonstrate that the improved model has a strong identification capability. The detection average precision reach by 92.1%, which can meet can meet the precision demands in actual situations.

The existing coal mine safety helmet detection method has problems such as low detection accuracy, susceptibility to environmental impact, poor real-time performance, and a large number of parameters. So, this paper proposes a Miner Helmet detection algorithm based on YOLO, abbreviated as MH-YOLO. First, the convolutional block attention mechanism (CBAM) is applied to improve the CSPDarkNet53 to 2-Stage FPN (C2f) module of the backbone network and enhance feature-extraction capability. Second, the MaxPooling (MP) module is used to replace the partial subsampling convolution of YOLOv8 to reduce the impact of unbalanced sample categories and improve the recall rate. In addition, a small target detection layer is added to further improve the small target characteristics by fusing shallow network features with deep network features. Finally, the ZoomCat and Scalseq Module (ZAS) feature-extraction module is used to improve the detection accuracy of small and overlapping targets. Training and testing were conducted on the public dataset CUMT-Helmet from China University of Mining and Technology and DsLMF + helmet from Xi’an University of Science and Technology. The proposed MH-YOLO achieves mAP50 values of 92.4% and 97.8%, respectively, surpassing the comparative networks. The detection time is 10.1 ms, enabling accurate and real-time detection of whether coal miners are wearing safety helmets. Source code is released in https://github.com/xgli411/MH-YOLO .

In high-risk industrial environments such as tunnel construction, reliable safety helmet detection is critical for preventing head injuries. However, severe illumination inhomogeneity and multi-scale object appearances pose significant challenges to existing detectors due to static anchor designs and the absence of illumination-aware feature learning. This paper proposes AE-LFOG-YOLO, an end-to-end framework that enhances YOLOv8 through dual physics-informed optimizations. The approach integrates an Illumination-Invariant Module (IIM) that employs a dual-path feature decoupling strategy to suppress lighting artifacts within the network backbone. Concurrently, the Adaptive Evolutionary - Light Field Optimized Generation (AE-LFOG) algorithm replaces static anchors with a dynamic evolutionary process guided by local illumination gradients and thin-lens imaging principles, enabling continuous optimization of anchor parameters during training. Evaluated on a real-world tunnel dataset, the method achieves 94.83% mAP@0.5 and significantly improves robustness under challenging illumination variations, as evidenced by a 35.7% extension in effective operating range. These results demonstrate the effectiveness of integrating physical imaging priors into deep learning for robust visual perception in complex industrial scenarios.

Monitoring the safety of underground work environments is a fundamental aspect of industrial safety production. Detecting safety helmet wearing, as a critical task, poses numerous challenges, including interference caused by complex backgrounds, low-light conditions, and insufficient detection accuracy achieved for small targets. Existing methods exhibit limitations in terms of multiscale feature fusion, foreground localization accuracy, and dynamic context modeling. Traditional feature pyramids struggle to resolve cross-scale feature conflicts, whereas fixed dilated convolutions lack adaptability in complex scenes. To address these issues, a YOLOv11-SRA model based on the YOLOv11 architecture is proposed in this paper; the model is integrated with a three-stage optimization strategy: the SAConv module dynamically adjusts the dilation rates to capture multiscale contextual information, thereby enhancing the robustness of small target detection. The RCM employs rectangular self-calibrated attention to refine the foreground region, improving the boundary localization capabilities of the model. The ASFF module fuses multiscale features through adaptive spatial weighting to alleviate feature conflicts. The effectiveness of the algorithm was validated based on a publicly available underground safety helmet dataset, i.e., CUMT-HelmeT, where it achieved a mean average precision at an IoU of 0.50 (mAP50) of 84.2% and a recall of 79.9%, significantly outperforming the mainstream models.

Automated safety monitoring on construction sites requires precise helmet-status detection and robust multi-object tracking in long, occlusion-rich video sequences. This study proposes a two-stage framework: (i) a YOLOv5 model enhanced with self-adaptive coordinate attention (SACA), which incorporates coordinate-aware contextual information and reweights spatial–channel responses to emphasize head-region cues—SACA modules are integrated into the backbone to improve small-object discrimination while maintaining computational efficiency; and (ii) a DeepSORT tracker equipped with fuzzy-logic gating and temporally consistent update rules that fuse short-term historical information to stabilize trajectories and suppress identity fragmentation. On challenging real-world video footage, the proposed detector achieved a mAP@0.5 of 0.940, surpassing YOLOv8 (0.919) and YOLOv9 (0.924). The tracker attained a MOTA of 90.5% and an IDF1 of 84.2%, with only five identity switches, outperforming YOLOv8 + StrongSORT (85.2%, 80.3%, 12) and YOLOv9 + BoT-SORT (88.1%, 83.0%, 10). Ablation experiments attribute the detection gains primarily to SACA and demonstrate that the temporal consistency rules effectively bridge short-term dropouts, reducing missed detections and identity fragmentation under severe occlusion, varied illumination, and camera motion. The proposed system thus provides accurate, low-switch helmet monitoring suitable for real-time deployment in complex construction environments.