Fig. 1. Industrial inspection FPV drone (quadcopter) with protective frame
Владислав Витальевич Егельский, аспирант кафедры «Эксплуатация транспортных систем и логистика»
344003, г. Ростов-на-Дону, пл. Гагарина
Vladislav V. Egelsky, Postgraduate Student of the Department of Operation of Transport Systems and Logistics
1, Gagarin Sq., Rostov-on-Don, 344003
Николай Николаевич Николаев, кандидат технических наук, доцент кафедры «Эксплуатация транспортных систем и логистика»
344003, г. Ростов-на-Дону, пл. Гагарина
Nikolai N. Nikolaev, Cand. Sci. (Eng.), Associate Professor of the Department of Operation of Transport Systems and Logistics
1, Gagarin Sq., Rostov-on-Don, 344003
Елена Владимировна Егельская, кандидат технических наук, доцент кафедры «Эксплуатация транспортных систем и логистика»
344003, г. Ростов-на-Дону, пл. Гагарина
Elena V. Egelskaya, Cand. Sci. (Eng.), Associate Professor of the Department of Operation of Transport Systems and Logistics
1, Gagarin Sq., Rostov-on-Don, 344003
Анатолий Аркадьевич Короткий, доктор технических наук, профессор, заведующий кафедрой «Эксплуатация транспортных систем и логистика»
344003, г. Ростов-на-Дону, пл. Гагарина
Anatoly A. Korotkiy, Dr. Sci. (Eng.), Professor, Head of the Transport Systems Operation and Logistics Department
1, Gagarin Sq., Rostov-on-Don, 344003
Леонид Александрович Терновской, аспирант кафедры «Эксплуатация транспортных систем и логистика»
344003, г. Ростов-на-Дону, пл. Гагарина
Leonid A. Ternovskoi, Postgraduate Student of the Department of Transport Systems and Logistics
1, Gagarin Sq., Rostov-on-Don, 344003
Introduction. Inspecting the overhead crane runways in production workshops and warehouses is a challenging and dangerous task. The risks for specialists are associated with the high altitude at which the runways are located and the lack of walkways along them. At the same time, standard visual and dimensional inspection is characterized by a low inspection speed. The scientific literature contains numerous studies on the potential of artificial intelligence (AI) to ensure occupational safety: methods for monitoring occupational risks and preventing accidents, the relationship between accident rates and the competencies of crane operators, the detection of defects in removable load‑handling devices using computer vision tools, as well as remote monitoring of crane safety based on video data from IP cameras. However, these solutions do not address the inspection of crane runways, which have specific features, primarily their considerable length (often up to 200 meters or more). The application of AI requires video-analytical monitoring along the entire length of the runway and training a neural network to recognize local defects and geometric deviations, which existing methods do not provide. Today, crane runway inspection is conducted through visual and dimensional control methods, including direct inspection and surveying using total stations and theodolites. While these methods are accurate, they are labor-intensive and time-consuming. Therefore, there is a need for the use of AI to improve safety and speed while maintaining accuracy in identifying faults in crane runways. The aim of this research is to develop a method for remote inspection of the overhead crane runways located at height in industrial facilities. This will help minimize the exposure of workers to hazardous and harmful working conditions while maintaining accuracy, speed, and reliability of the inspection results.
Materials and Methods. Data on crane runway defects collected during inspections at industrial facilities was used as the basis for the study. The methodology for identifying defects in crane runways was based on GOST R 56944–20161. Computer vision neural networks were trained using open libraries for Python language. A modernized pre-trained YOLOv8 neural network was used to detect defects.
Results. A method for remote detection of defects on overhead crane runways was developed using an unmanned aerial vehicle designed by the authors (a quadcopter with a protective frame, equipped with a Livox MID‑40 lidar, an Orbbec Gemini 2 depth camera, a 4K RGB camera, and a DWM1000 positioning system supporting TWR and TDOA). Based on survey data collected in 2024 from 352 overhead cranes with a total runway length of approximately 14 kilometers, a modernized YOLOv8 neural network for computer vision was trained. This resulted in the creation of a complete three-dimensional point cloud that covered the runways, crane beams and supports, as well as reference to column lines and centers. The three-dimensional model allowed for the automatic identification and classification of local defects, as well as the estimation of their sizes with accuracy of one millimeter. Automated geometry assessment showed that the deviations in the runway markings in the model in question did not exceed the permitted values according to GOST R 56944–2016 (40 millimeters in one section and 10 millimeters on adjacent columns), which confirmed the effectiveness of the method.
Discussion. The results obtained indicate that the authors’ goal has been achieved — the development of a method for remote inspection of overhead crane runways. This was made possible by conducting a significant number of surveys, which provided a diverse range of defects for training neural networks. A comparison with previous studies has shown the uniqueness of the proposed approach to inspecting overhead crane runways. Methods based on artificial intelligence and unmanned aerial vehicles (UAVs) have previously been used to monitor personnel, assess removable lifting attachments, and inspect tower cranes outdoors. However, these methods were not suitable for detecting local defects in overhead crane runways inside production facilities. The main limitation of the developed method was the flight time of the UAV (no more than 20 minutes), which was due to the low battery capacity. This capacity could not be increased without increasing the maximum size of the device (0.5 meters) in the confined conditions of enclosed spaces. The new method's results were positive, as it enabled inspection of tracks along their entire length from a close distance. By building a 3D model using photogrammetry, it was possible to assess the size of defects and reduce labor intensity and duration of the survey by at least half. These benefits made its further development and practical implementation worthwhile.
Conclusion. The main outcome of the study was the development of a method for remote inspection of overhead crane runways located at height in industrial facilities. During the research, neural networks were trained to detect defects, and an algorithm was created for inspection. This involved creating a three-dimensional model that allowed for automated assessment of geometric deviations in the runways in both longitudinal and transverse planes, as well as the identification of local defects. The key benefit of this method was that it eliminated the need for experts to climb to heights, ensuring their safety. Additionally, it allowed for the inspection of hard-to-reach areas and the detection of previously invisible defects, reducing the likelihood of future emergency situations. Further research in this field will focus on improving the system's ability to automatically identify the condition of load-bearing metal structures and possible defects in cranes.
Введение. Обследование крановых путей мостовых кранов в производственных цехах и на складах — сложная и небезопасная задача. Риск для специалистов связан с большой высотой, на которой расположены пути, а также с тем, что проходные галереи вдоль них часто отсутствуют. При этом стандартный визуально-измерительный контроль отличается низкой скоростью обследования. В научной литературе широко представлены исследования, в которых рассматриваются возможности искусственного интеллекта (ИИ) для обеспечения производственной безопасности: методы контроля производственных рисков и предотвращения аварий, взаимосвязь аварийности с компетенциями машинистов кранов, распознавание дефектов съемных грузозахватных приспособлений средствами компьютерного зрения, а также дистанционный мониторинг безопасности кранов по данным видео с IP-камер. Однако эти решения не касаются обследования самих крановых путей, которые обладают рядом специфических особенностей — прежде всего значительной протяженностью (нередко до 200 метров и более). Применение ИИ здесь требует видеоаналитического контроля по всей длине пути и обучения нейросети распознаванию специфических местных дефектов и отклонений геометрии, чего существующие методы не обеспечивают. Сегодня такое обследование выполняется в форме визуально-измерительного контроля с непосредственным осмотром и планово-высотной съемкой тахеометрами и теодолитами. Эти методы точны, но трудоемки и медленны. Таким образом, существует запрос на применение ИИ для идентификации неисправностей крановых путей с сохранением точности при повышении безопасности и скорости. Цель данного исследования — разработка метода дистанционного обследования крановых путей мостовых кранов, расположенных на высоте в производственных помещениях, позволяющего минимизировать воздействие опасных и вредных производственных факторов на персонал при сохранении точности, скорости и достоверности получаемых результатов.Материалы и методы. В качестве базовой информации были использованы данные о дефектах крановых путей, полученные в ходе обследования их на промышленных предприятиях. Методическая часть по выявлению дефектов крановых путей основана на ГОСТ Р 56944–20161. Нейронные сети компьютерного зрения обучали на основе открытых библиотек для языка Python. Для распознавания дефектов использовалась модернизированная предобученная нейронная сеть YOLOv8. Результаты исследования. Разработан метод дистанционной дефектации надземных крановых путей мостовых кранов с применением беспилотного летательного аппарата авторской конструкции (квадрокоптер с защитным каркасом, оснащённый лидаром Livox MID-40, камерой глубины Orbbec Gemini 2, RGB-камерой 4K и системой позиционирования DWM1000 с поддержкой TWR и TDOA). На основе данных обследования 352 надземных крановых путей общей протяжённостью около 14 километров, полученных в 2024 году, была обучена модернизированная нейронная сеть компьютерного зрения YOLOv8. В результате было создано целостное трёхмерное облако точек, охватывающее рельсы, подкрановые балки и опоры с привязкой к створам и осям колонн. Построенная трёхмерная модель позволила в автоматизированном режиме выявить и классифицировать локальные дефекты и оценить их размеры с точностью до одного миллиметра. Автоматизированная оценка геометрии показала, что отклонения отметок рельсов в рассматриваемой модели не превышают допустимые по ГОСТ Р 56944–20162 значения (40 миллиметров в одном сечении и 10 миллиметров на соседних колоннах), что подтвердило работоспособность метода.Обсуждение. Полученные результаты свидетельствуют о достижении поставленной авторами цели — о разработке метода дистанционного обследования крановых путей мостовых кранов. Этому способствовал значительный объём проведённых обследований, обеспечивший разнообразную выборку дефектов для обучения нейронных сетей. Сравнение с ранее проведенными исследованиями показало уникальность предложенного подхода к обследованию путей мостовых кранов: уже описанные методы на основе искусственного интеллекта и БПЛА применялись для контроля персонала, оценки съёмных грузозахватных приспособлений, стальных канатов или для обследования башенных кранов на открытом воздухе, однако ни один из них не был пригоден для выявления локальных дефектов надземных крановых путей внутри производственных помещений. Основным ограничением разработанного метода является время полёта БПЛА (не более 20 минут), обусловленное малой ёмкостью аккумуляторов, которую без повышения предельного размера аппарата (0,5 метра) невозможно увеличить в стеснённых условиях закрытых помещений. Результаты разработки нового метода интерпретируются как положительные: он обеспечивает осмотр путей по всей длине с близкого расстояния, построение 3D-модели методом фотограмметрии позволяет оценить размеры дефектов и снизить трудоёмкость и продолжительность обследования не менее чем в два раза. Все это делает целесообразным его дальнейшее развитие и внедрение в практику.Заключение. Основным итогом исследования стал разработанный метод дистанционного обследования крановых путей мостовых кранов, расположенных на высоте в производственных помещениях. В ходе проделанной работы были обучены нейронные сети поиска дефектов и создан алгоритм обследования с построением трёхмерной модели, обеспечивающей автоматизированную оценку геометрических отклонений рельсов в продольной и поперечной плоскостях и выявление локальных дефектов. Ключевое достоинство метода — обеспечение безопасности экспертов за счёт исключения подъёма на высоту, а также возможность обследования труднодоступных участков и обнаружения ранее незаметных дефектов, что снижает вероятность возникновения впоследствии аварийных ситуаций. Дальнейшие исследования в этом плане будут направлены на дополнение системы функцией автоматической идентификации состояния металлоконструкций самих грузоподъёмных кранов и их возможных дефектов.
Introduction. The inspection of overhead crane runways in production facilities and warehouses is a complex and risky task for experts and professionals in the field of lifting equipment. Currently, the inspection of overhead crane runways is conducted in accordance with Rostechnadzor Order No. 461 dated November 26, 2020 “On the Approval of Federal Standards and Regulations in the Field of Industrial Safety “Safety Rules for Hazardous Production Facilities with Lifting Facilities”2. The inspection involves visual and measuring control along the entire length of the crane runway, as well as geometric parameter control during the planned compilation survey using special devices such as total stations and theodolites. The danger during this inspection arises from the fact that crane runways are located at a considerable height, usually ranging from 3–5 meters, but in some cases exceeding 20 meters. Overhead cranes come in two types, depending on how they are supported on the crane tracks. There are supported cranes and suspended cranes. In the supported type, the runways are attached to the crane beam, which is most often an I-beam, by welding or bolting, and these joints are prone to wear. When heavy-lift cranes are installed, connecting passages may be arranged to allow for inspection of the supporting crane runways. However, if the lifting capacity is less than 10 tons, connecting passages may not be provided. With suspended cranes (where the crane runways are in the form of I-beams), connecting passages are also typically not provided. In such cases, inspection is conducted from repair sites and using scaffold towers, which greatly increases the difficulty and duration of the inspection, as well as the risk of falling from height. Therefore, it is necessary to develop examination methods that do not require specialists and experts to be lifted to a height, while ensuring the accuracy of detecting and evaluating defects. Additionally, there is a challenge in developing faster and more efficient control methods than direct visual inspection.
The scientific literature has already discussed the potential of artificial intelligence for industrial safety. One well-known platform is a digital video monitoring system [
A method for LHD assessment based on computer vision algorithms trained using the Roboflow platform has been developed and applied [
In foreign studies [
It is known that unmanned aerial vehicles (UAVs) are used for visual inspection of hard-to-reach areas of tower crane structures [
The authors of [
There are studies on the integral assessment of the risk of operating steel ropes using computer vision technologies [
There are several UAVs that are designed specifically for use indoors and in enclosed spaces [
An analysis of the published results of such studies has revealed a gap in scientific knowledge regarding the lack of an effective survey method for crane runways located at a height inside industrial premises. This method should identify defects in crane tracks with sufficiently high accuracy and inspection speed, while ensuring the safety of personnel. This method could be developed by integrating the scientific knowledge presented in previous research, adding unique and essential components.
This research aims to develop a method for remote inspection of overhead crane tracks located at a height in industrial premises, which will minimize the impact of hazardous and harmful production factors on technical personnel and ensure the accuracy, speed and reliability of the results.
To achieve this goal, it is necessary to solve the following tasks:
Materials and Methods. Data on defects on overhead crane runways was used as initial information. This data was obtained during the inspection of overhead cranes as part of expert control of lifting cranes at machine-building enterprises in 2024. A survey was conducted during which 352 overhead crane runways with a total length of 14 kilometers were examined. The methodological part of the study was based on GOST R 56944–20163. Computer vision neural networks were trained using open libraries for the Python language. An upgraded YOLOv8 pre-trained neural network was used to detect defects. Its architecture was a three-part system consisting of a feature extractor (CSPDarknet53 software module), a feature combinator (C2f module) and an output predictive block (decoupled head module). YOLOv8 was developed by Ultralytics and had open source code available for further modernization to meet the needs of a specific project.
The hardware basis of the platform was a flying mobile system with a modular architecture (Fig. 1)
Fig. 1. Industrial inspection FPV drone (quadcopter) with protective frame
The authors proposed their own design of an unmanned aerial vehicle (quadcopter), assembled from the components manufactured in China and available for purchase from the distributors of such equipment.
To position UAVs relative to base stations and so-called anchors, the DWM1000 sensor was used — TWR positioning support (Two-Way Ranging, a method used to determine the distance between two devices using signal exchange) and the TDOA sensor (Time Difference of Arrival, a positioning method that determines the location of an object by measuring the difference in the time of arrival of its signal by several receiving stations (anchors) with known coordinates).
The sensor complex included three key types of sensors:
– spatial sensing equipment (Livox MID-40 multi-lens lidar, Orbbec Gemini 2 depth camera);
– visual perception equipment (DJI O3 Air Unit— a digital FPV system that included a compact 4K RGB camera and a transmitter);
– positioning and stabilization systems (DWM1000 sensors, NAV40 inertial modules, short-range detection systems based on ArduPilot firmware, backup navigation methods — visual odometry and optical navigation based on lidar and cameras).
The computing subsystem was based on the Aocoda-RC F765 V2 processor module with graphics acceleration, which supported the computer vision algorithms, neural network processing and data fusion.
The survey procedure for the above-ground crane runway of the supporting structure using an unmanned aerial vehicle was conducted in accordance with the following algorithm (Fig. 2):
Fig. 2. Algorithm for using UAVs to inspect overhead crane runways
Fig. 3. Primary (1) and close scanning area (2)
In paragraphs 8 and 9 of the algorithm, the accuracy of the detected deviations and defects at the test stage of the method was verified by comparison with the results of visual and measuring control. After completing the tests of the method, the parameters of its accuracy would be indicated in the regulatory and technical documentation.
Research Results. A method of flaw detection of overhead crane runways using unmanned aerial vehicles has been developed. Local defects of crane runways have been identified. The data were obtained during the survey of overhead cranes as part of an expert survey of lifting cranes at machine-building enterprises.
In particular, the following tasks set by the authors were solved:
Fig. 4. Model of a beam crane with crane runways
Figure 4 demonstrates a model of an overhead beam crane with crane runways obtained from a three-dimensional point cloud, the axes of A and B rails are indicated, as well as the line numbers — columns with a median line between them.
The photographs of the defects shown in Figures 5–10 were taken in the workshops of an industrial company during a real-life survey, using the available lighting and optical clarity of the air.
The defects were assessed using a trained computer vision neural network.
Figure 5 shows a defect in the joint of an I-beam with poor-quality welding of the fixing plate.
Fig. 5. Joint defect
Figure 6 demonstrates a defect in the perforation of the I-beam of a suspended overhead crane (in the weld area).
Fig. 6. I-beam perforation
Figure 7 shows an example of rail wear with chipping of the side edges and holes on the work surface.
Fig. 7. Wear of the rail with chipping and holes
Figure 8 provides the result of abrasion of the I-beam by the wheel of an overhead crane. The defect could occur as a result of bending of the beam or malfunction of the crane structures.
Fig. 8. Abrasion of the side surface of the I-beam
Figure 9 shows the wear of the rail as a result of wheel slipping of the support crane.
Fig. 9. Wear of the rail as a result of the crane wheel slipping
Figure 10 shows a defect in the thermic cut of the I-beam without a bottom plate.
Fig. 10. Defect in the thermic cut area of the I-beam
Evaluation of the geometry of crane runways, presented as a model in Figure 4, gave the following results, which are shown on Figure 11.
Fig. 11. Evaluation of the geometry of rails in the vertical plane
The permissible difference in rail elevation within one cross-section along the axis of symmetry [P1] was 40 mm, and the permissible difference between adjacent columns [P2] was 10 mm (according to GOST R 56944–20165). By comparing the data in Figure 11 to the allowable values, we could conclude that the geometry deviations in the crane track under consideration were within tolerance.
This method of flaw detection allowed us to obtain photo and video data, which could be used to estimate the size of defects with accuracy of one millimeter.
Discussion. The results obtained during the application of the method developed by the authors fully meet both the research goals and the objectives of its implementation. Achieving accurate and precise indicators was made possible by the large number of above-ground crane runway surveys, which allowed us to collect a variety of samples of defects in the geometry of crane runways and local defects. Data processing made it possible to train neural networks for computer vision and create fully functional three-dimensional models that can automatically identify and evaluate defects.
Comparison of the results obtained with literature data, theories, and other developments allows us to conclude that this study is unique. No such method has been developed before for overhead crane runways.
At the moment, the main disadvantage of the developed method is the limited operation time of an unmanned aerial vehicle due to a lack of battery power. It is not possible to install larger batteries, as this would require increasing the size of the unmanned aerial vehicle. Therefore, the maximum size of a drone can be no more than 0.5 meters, due to the limited space in enclosed areas, and the flight time can be no longer than 20 minutes (depending on the weight of the equipment).
The results of the study should be interpreted as positive. The method should be developed and implemented to improve the efficiency of inspection of overhead crane runways. In practice, the method allows you to:
— inspect crane runways from a close distance along their entire length;
— estimate the size of local defects using photo and video recordings and specialized software;
— create a 3D model of the crane runway using photogrammetry and assessing deviations from straightness in both the longitudinal and transverse planes;
— reduce labor intensity and duration of inspections by at least two times.
Conclusion. In conclusion, it should be noted that the work done by the authors has produced significant results, the most important of which is the development of a method for remote inspection of overhead crane runways. During the study, neural networks were trained to detect defects and an algorithm was created for examining crane runways by creating a three-dimensional model that allows for estimating geometric deviations in the rails in the longitudinal and transverse planes. The main advantage of this method is that it ensures the safety of experts and specialists who inspect lifting facilities. Additionally, the method enables the inspection of hard-to-access areas of crane runways and the identification of defects that may have gone unnoticed, reducing the likelihood of accidents.
Future research plans for lifting cranes involve the development of additional features to the system that will help identify their condition. The system will automatically inspect the crane runways and analyze the condition of the metal structures, identifying any defects.
1. GOST R 56944–2016 “Lifting Cranes. Overhead Crane Rail Tracks. General Technical Requirements” (approved and put into effect by Order No. 463‑st of the Federal Agency for Technical Regulation and Metrology dated 1 June 2016). URL: https://ohranatruda.ru/upload/iblock/8fe/4293754222.pdf?ysclid=mnsrn94qrk671517049 (accessed: 10.04.2026). 2. Rostechnadzor Order No. 461 dated November 26, 2020 “On the Approval of Federal Standards and Regulations in the Field of Industrial Safety “Safety Rules for Hazardous Production Facilities with Lifting Facilities”. (In Russ.) URL: https://www.consultant.ru/document/cons_doc_LAW_373321/ (accessed: 14.07.2026). 3. GOST R 56944–2016 “Lifting Cranes. Overhead Crane Rail Tracks. General Technical Requirements” (approved and put into effect by Order No. 463‑st of the Federal Agency for Technical Regulation and Metrology dated 1 June 2016). URL: https://ohranatruda.ru/upload/iblock/8fe/4293754222.pdf?ysclid=mnsrn94qrk671517049 (accessed: 10.04.2026). 4. GOST R 56944–2016 “Lifting Cranes. Overhead Crane Rail Tracks. General Technical Requirements” (approved and put into effect by Order No. 463‑st of the Federal Agency for Technical Regulation and Metrology dated 1 June 2016). URL: https://ohranatruda.ru/upload/iblock/8fe/4293754222.pdf?ysclid=mnsrn94qrk671517049 (accessed: 10.04.2026). 5. GOST R 56944–2016 “Lifting Cranes. Overhead Crane Rail Tracks. General Technical Requirements” (approved and put into effect by Order No. 463‑st of the Federal Agency for Technical Regulation and Metrology dated 1 June 2016). URL: https://ohranatruda.ru/upload/iblock/8fe/4293754222.pdf?ysclid=mnsrn94qrk671517049 (accessed: 10.04.2026).
The authors declare that there are no conflicts of interest present.