Search Results (21)

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

Low-altitude drones, which are unimpeded by traffic congestion or urban terrain, have become a critical asset in emergency rescue missions. To address the current lack of emergency rescue data, UAV aerial videos were collected to create an experimental dataset for action classification and localization annotation. A total of 5082 keyframes were labeled with 1–5 targets each, and 14,412 instances of data were prepared (including flight altitude and camera angles) for action classification and position annotation. To mitigate the challenges posed by high-resolution drone footage with excessive redundant information, we propose the SlowFast-Traffic (SF-T) framework, a spatio-temporal sequence-based algorithm for recognizing traffic accident rescue actions. For more efficient extraction of target–background correlation features, we introduce the Actor-Centric Relation Network (ACRN) module, which employs temporal max pooling to enhance the time-dimensional features of static backgrounds, significantly reducing redundancy-induced interference. Additionally, smaller ROI feature map outputs are adopted to boost computational speed. To tackle class imbalance in incident samples, we integrate a Class-Balanced Focal Loss (CB-Focal Loss) function, effectively resolving rare-action recognition in specific rescue scenarios. We replace the original Faster R-CNN with YOLOX-s to improve the target detection rate. On our proposed dataset, the SF-T model achieves a mean average precision (mAP) of 83.9%, which is 8.5% higher than that of the standard SlowFast architecture while maintaining a processing speed of 34.9 tasks/s. Both accuracy-related metrics and computational efficiency are substantially improved. The proposed method demonstrates strong robustness and real-time analysis capabilities for modern traffic rescue action recognition. Full article

An automatic identification system (AIS) is often installed on merchant ships and fishing boats to prevent collisions and ensure safe navigation. The location information of ships transmitted from AIS equipment can help maritime traffic control prevent accidents. The southern coast of Korea comprises a complex coastline with numerous fishing boats and transit vessels. In particular, the Tongyeong and Geoje Islands include high-altitude mountains and islands, resulting in several radio-shaded areas where AIS signals cannot be received, owing to geographical effects. However, only a few studies have explored this region and performed practical experiments on the reception status of AIS locations in radio-shaded areas. In this study, we performed an experiment in the Geoje Island Sea on the southern coast to analyze the impact of high terrain on the reception rate and status of automatic identification devices. Two identical pieces of AIS equipment were installed to generate multiple radio waves, and the location data transmitted via different antennae were compared. The experimental analysis forms the basis for identifying the exact location of ships in the event of maritime accidents, facilitating rapid rescue. Moreover, the accuracy of the location transmitted by the AIS equipment can aid in detecting the cause of accidents. Full article

With the rapid development of the shipping industry, the number of ships is continuously increasing, and maritime accidents happen frequently. In recent years, computer vision and drone flight control technology have continuously developed, making drones widely used in related fields such as maritime target detection. Compared to the cameras fixed on ships, a greater flexibility and a wider field of view is provided by cameras equipped on drones. However, there are still some challenges in high-altitude detection with drones. Firstly, from a top-down view, the shapes of ships are very different from ordinary views. Secondly, it is difficult to achieve faster detection speeds because of limited computing resources. To solve these problems, we propose YOLOv7-DyGSConv, a deep learning-based model for detecting ships in real-time videos captured by drones. The model is built on YOLOv7 with an attention mechanism, which enhances the ability to capture targets. Furthermore, the Conv in the Neck of the YOLOv7 model is replaced with the GSConv, which reduces the complexity of the model and improves the detection speed and detection accuracy. In addition, to compensate for the scarcity of ship datasets in top-down views, a ship detection dataset containing 2842 images taken by drones or with a top-down view is constructed in the research. We conducted experiments on our dataset, and the results showed that the proposed model reduced the parameters by 16.2%, the detection accuracy increased by 3.4%, and the detection speed increased by 13.3% compared with YOLOv7. Full article

The statistical analysis of civil aircraft accidents reveals that the highest incidence of mishaps occurs during the approach and landing stages. Predominantly, these accidents are marked by abnormal energy states, leading to critical situations like stalling and heavy landings. Therefore, it is of great significance to accurately predict the aircraft energy state in the approach and landing stages to ensure a safe landing. In this study, a deep learning method based on time sequence data for the prediction of the aircraft approach and landing energy states is proposed. Firstly, by conducting an extensive overview of the existing literature, three characteristic parameters of altitude, velocity, and glide angle were selected as the indicators to characterize the energy state. Following this, a semi-physical simulation platform for a certain type of aircraft was developed. The approach and landing experiments were carried out with different throttle sizes and flap deflection under different wind speeds and wind directions. Then, a deep learning prediction model based on Long Short-Term Memory (LSTM) was established based on the experimental data to predict the energy state indicators during the approach and landing phases. Finally, the established LSTM model underwent rigorous training and testing under different strategies, and a comparative analysis was carried out. The results demonstrated that the proposed LSTM model exhibited high accuracy and a strong generalization ability in predicting energy states during the approach and landing phases. These results offer a theoretical basis for designing energy early warning systems and formulating the relevant flight control laws in the approach and landing stages. Full article

Meteorological disasters on highways can significantly reduce road traffic efficiency. Low visibility caused by dense fog is a severe meteorological disaster that greatly increases the incidence of traffic accidents on highways. Accurately predicting highway visibility and taking timely countermeasures can mitigate the impact of meteorological disasters and enhance traffic safety. This paper introduces the ATCNet model for highway visibility prediction. In ATCNet, we integrate Transformer, Capsule Networks (CapsNet), and self-attention mechanisms to leverage their respective complementary strengths. The Transformer component effectively captures the temporal characteristics of the data, while the Capsule Network efficiently decodes the spatial correlations and hierarchical structures among multidimensional meteorological elements. The self-attention mechanism, serving as the final decision-refining step, ensures that all key temporal and spatial hierarchical information is fully considered, significantly enhancing the accuracy and reliability of the predictions. This integrated approach is crucial in understanding highway visibility prediction tasks influenced by temporal variations and spatial complexities. Additionally, this study provides a self-collected publicly available dataset, WD13VIS, for meteorological research related to highway traffic in high-altitude mountain areas. This study evaluates the model’s performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE). Experimental results show that our ATCNet reduces the MSE and MAE by 1.21% and 3.7% on the WD13VIS dataset compared to the latest time series prediction model architecture. On the comparative dataset WDVigoVis, our ATCNet reduces the MSE and MAE by 2.05% and 5.4%, respectively. Our model’s predictions are accurate and effective, and our model shows significant progress compared to competing models, demonstrating strong universality. This model has been integrated into practical systems and has achieved positive results. Full article

High-altitude work poses significant safety risks, and wearing safety belts is crucial to prevent falls and ensure worker safety. However, manual monitoring of safety belt usage is time consuming and prone to errors. In this paper, we propose an improved high-altitude safety belt detection algorithm based on the YOLOv8 model to address these challenges. Our paper introduces several improvements to enhance its performance in detecting safety belts. First, to enhance the feature extraction capability, we introduce a BiFormer attention mechanism. Moreover, we used a lightweight upsampling operator instead of the original upsampling layer to better preserve and recover detailed information without adding an excessive computational burden. Meanwhile, Slim-neck was introduced into the neck layer. Additionally, extra auxiliary training heads were incorporated into the head layer to enhance the detection capability. Lastly, to optimize the prediction of bounding box position and size, we replaced the original loss function with MPDIOU. We evaluated our algorithm using a dataset collected from high-altitude work scenarios and demonstrated its effectiveness in detecting safety belts with high accuracy. Compared to the original YOLOv8 model, the improved model achieves P (precision), R (recall), and mAP (mean average precision) values of 98%, 91.4%, and 97.3%, respectively. These values represent an improvement of 5.1%, 0.5%, and 1.2%, respectively, compared to the original model. The proposed algorithm has the potential to improve workplace safety and reduce the risk of accidents in high-altitude work environments. Full article

Railway system insulators are affected by pollution, altitude, and other environmental situations during operation, which causes reduced electrical performance or even flashover accidents. These factors threaten the safety of railway operations in high-altitude areas. However, the natural contamination characteristics of a railroad in a plateau area is still unclear. In this study, a natural pollution accumulation test for railway insulators in a high-altitude area was carried out, and the distribution rules of nonsoluble deposit density (NSDD), equivalent salt deposit density (ESDD), NSDD/ESDD ratio, and nonuniformity (T/B) of the pollution distribution of the tested insulators were calculated. Meanwhile, the chemical compositions of the pollution from different test sites were analyzed. The differences of pollution accumulation between railway insulators and suspended insulators of a power system and the influencing factors were compared and analyzed by combining with a numerical simulation. The results show that the pollution level of railroad insulators is mainly distributed in level b and above. A pollution sample is mainly composed of sodium chloride, while the NSDD/ESDD ratio of pollution is mostly distributed from 0 to 5, with T/B value ranges from 1/0.62 to 1/1.76. The amount of insulator contamination is influenced by the location inside and outside the tunnel. Additionally, the pollution amount is influenced by the structure and type of insulators. Finally, this paper studies the creepage distance and structural height required by railway insulators in a plateau area according to the natural pollution accumulation characteristics of railway insulators, which can provide a reference for a railway electrical external insulation configuration in a high-altitude area. Full article

Snakebites can pose a significant threat to human health as the destruction of natural habitats and increased human intrusion into ecosystems result in more frequent encounters with snakes. Mitigation measures for snakebites are particularly crucial for hiking trails where transportation of snakebite victims to medical facilities is challenging due to limited emergency resources and difficult access. This study employed a random forest-based species distribution model approach to investigate the potential habitats of Gloydius spp., specifically Gloydius saxatilis, Gloydius brevicaudus, and Gloydius ussuriensis, in South Korea and to assess the snakebite risk in national parks. Potential habitats of Gloydius spp. were identified and visualized by overlaying binary maps derived from species distribution models (SDMs) of each Gloydius spp. that corresponded to high-risk snakebite areas. In addition, hiking trails with high snakebite risk in the national parks were identified after demonstrating the statistical correlation between the potential habitat distribution of Gloydius spp. and the actual snakebite incidents in major regions of South Korea. The primary environmental variables determining Gloydius spp. habitat were the topographic position index, slope, and the annual average of the maximum and minimum temperatures. The potential habitat of G. saxatilis generally appeared in high-altitude mountainous areas, mostly in the eastern part of the study area. Favorable habitats for G. brevicaudus and G. ussuriensis were predominantly located in mountainous areas throughout the study area, with the exception of some high-altitude mountainous terrain in the east. The number of snakebite incidents per 10,000 people was significantly correlated with the area ratio of Gloydius spp. potential habitat (Spearman’s rho = 0.638, p < 0.01). The proportion of snakebite risk areas among national parks in South Korea ranged from 18% to 57%. This study can support practical solutions to prevent injuries and fatalities among hikers due to snakebites by identifying areas with a high risk of snakebite accidents at the hiking-trail level. Full article

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. Full article

Background: High-altitude mountaineering has become more and more popular. While many enjoy the beauty of the highest parts of Switzerland, there are considerable risks, which can even result in death. This study analyzed fatal events while high-altitude mountaineering in the Swiss Alps. Materials and Methods: In this study, cases of emergencies while high-altitude mountaineering in the Swiss Alps were analyzed in the period from 2009 to 2021 from the Swiss Alpine Club (SAC) emergency registry. Fatal emergencies were identified and analyzed in detail. Results: In total, 5020 emergency cases were analyzed, and among them 303 deathly events where detected. Of the fatal emergencies, 261 cases (86.1%) were male and 42 (13.9%) were female. The average age was 53.2 ± 19.1 years. More than half of the emergencies were on a route to a classic four-thousander. Fatal events were most common on the Matterhorn, with 40 cases (13.2%); on the Mönch, with 18 cases (5.9%); and on the Piz Bernina, with 10 cases (3.3%). In 245 of the fatal emergencies (80.9%), a fall was the cause. The second most prominent cause was rockfalls, with 16 cases (5.3%), followed by stranding, with 10 cases (3.3%), and avalanches, with 9 cases (3%). Illnesses and crevasse accidents counted together for less than 5% of the fatal cases. Almost two-thirds of fatal falls occurred while descending. Concerning nationality, 30% were from Switzerland and more than three-fourths of victims were from the countries of the Alps. Discussion: We found that falls were the most common cause of fatal emergencies in the Swiss Alps. Concerning the fact that most of these emergencies occurred during descents, fatigue and inadequate focus (forgetting the risks of the descent after successfully reaching the peak) are potential reasons for the fatal events. This potentially resulted from a lack of acclimatization, insufficient physical fitness, and inadequate tour planning. Since most victims were from the countries of the Alps, training tours may be possible as a recommended preparation for more difficult four-thousander peaks. Full article

There is an obvious tendency towards increasing the information content of surveys of hard-to-reach objects at high altitudes through the use of remote-controlled robot crawlers. This can be explained by the reasonable desire of industrial objects owners to maintain their property: pipelines, containers, metal structures in operating technical condition, which contributes to reducing accident risks and increasing the economic efficiency of operation (optimization of repair planning, etc.) This paper presents the concept of a robotic device equipped with LIDAR and EMAT which can move over pipes from a diameter of 100 mm by using a special type of magnetic wheel. The robot uses convolutional neural networks to detect structural elements and classify their defects. The article contains information about tests held on a specially developed test rig. The results showed that the device could increase the information level of survey and reduce the labour intensity. In this work, we consider a prototype of the device which has not started mass operation at industrial facilities yet. Full article

Background: Despite a potential high risk of acute mountain sickness (AMS) in the Swiss Alps, there is a lack of analyses concerning its relevance over longer periods. In consequence, the aim of this study is to analyze the prevalence of AMS in comparison to other causes of mountain emergencies in recent years in Switzerland. Material and Methods: Based on the central registry of mountain emergencies of the Swiss Alpine Club (SAC), all cases in the period between 2009 and 2020 were analyzed for AMS including the most severe forms of high-altitude pulmonary edema (HAPE) and high-altitude cerebral edema (HACE). Emergencies were assessed for the severity of the event with a National Advisory Committee for Aeronautics (NACA) score. Results: From a total of 4596 high-altitude mountaineering emergencies identified in the observational period, a total number of 352 cases of illnesses were detected. Detailed analysis revealed 85 cases of AMS, 5 cases of HAPE, and 1 case of HACE. The average altitude was 3845 ± 540 m. Most cases were in the canton of Valais, especially in the Monte Rosa region and the mountains of the Mischabel group (Täschhorn, Dom, Südlenz, Nadelhorn, Hohberghorn). There were only three deaths related to high-altitude illnesses; all the other events could be identified as moderate to severe but not life-threatening. Discussion: An emergency due to AMS that requires rescue is unlikely in the Swiss Alps. This does not imply that AMS is not a concern. However, the facts that the maximal altitude is relatively low and that fast self-descents often seem possible probably minimize the likelihood that mountaineers with symptoms contact emergency services. Full article

Analysis of the geological conditions of high-altitude and low-temperature stope slopes and the study of grade division are the basis for the evaluation of slope stability. Based on the engineering background of the eastern slope of the Preparatory iron mine in Hejing County, Xinjiang, we comprehensively analyse and summarize the factors that affect the geological conditions of high-altitude and cold slopes and finally determine nine geological conditions that affect the index parameters. Based on a back-propagation (BP) neural network algorithm, we establish an applicable network model to analyse the geological conditions of slopes in cold areas. The model is applied to the eastern slope to analyse and classify the geological conditions of the high-altitude and low-temperature slopes. The research results show that the skarn rock layer in the eastern slope is in a stable state and not prone to landslides, and its corresponding geological condition is Grade I; meanwhile, the monzonite porphyry rock layer is in a relatively stable state, with a potential for landslides and a corresponding geological condition Grade II. The marble rock layer is in a generally stable state, there is the possibility of landslide accidents, and the corresponding geological condition level is Grade III. The limestone rock layer is in an unstable state and prone to landslide accidents, it has a corresponding geology condition Grade IV. Therefore, the eastern slope can be divided into different geological condition regions: Zone I, Zone II, Zone III, and Zone IV, and the corresponding geological condition levels for these are Grade I, Grade II, Grade III, and Grade IV. These results may provide a basis for the stability evaluation of high altitudes and cold slopes. Full article

Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator T_i was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/T_i indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for T_i, which T_i indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved T_i can overcome these shortcomings; thus, T_i has the highest correlation. More data are needed to verify and support the study. Full article