The large-span transmission tower line system is an important lifeline power engineering facility, because of long-term exposure to the natural environment, it needs to face a variety of different complex environment, especially some complex environment, such as traffic, vehicles influence and lush vegetation influence and so on. Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. For environmental changes such as personnel, vehicles and illegal planting, some transmission towers are in remote locations, and the staff cannot find them in time. Aiming at the deficiency of artificial vision detection method, the research on the environmental perception technology of transmission tower based on deep learning is proposed. A large amount of data collected is trained, verified and tested with deep learning algorithm. In order to solve the problem of transmission towers exposed to complex environmental influence, an appropriate model was established based on deep learning algorithm, and the image was used to verify and test. The trained model was tested on some new images that were not used in the training and verification process. Experimental results show that this method can accurately identify the complex environmental objects.
Operation maintenance work is an important part for the structural health assessment of transmission tower. The routine management and maintenance work mainly relies on engineers and technicians with practical experience to carry out visual inspection and fill in the questionnaire. However, human based visual inspection is an arduous and time-consuming task, and its detection results largely depend on the subjective judgment of human inspectors, as the same time the workers working at height are very dangerous. Aiming at the deficiency of artificial vision detection method, a detection method of transmission tower component recognition based on image recognition is proposed. UAV is used to detect the transmission tower in an all-round way, Thousands of images are used to train, verify and test the convolutional neural network (CNN) classifier based on Alexanet. Aiming at the problem of damage identification of transmission tower components, fast R-cnn based on improved ZF network is trained, verified and tested by using images. The performance of the trained CNNs and Faster R-CNN is tested on some new images which are not used for training and validation processes. The test results show that the method can accurately identify the components and damage of transmission tower.
Roads are important parts of infrastructure. The detection of road condition plays an important role for the traffic safety. Vehicles, weather and other factors will cause different types of damage to the road surface. To avoid this happening, the commonly used method is manual inventory at present, which is time-consuming, laborious and easily leads to omissions. In order to overcome these shortcomings, this paper presents a method of road damage detection based on machine vision, which is more efficient and relatively cheap. To realize the method, the author used the Raspberry Pi, acceleration sensor, GPS module, Neural Compute Stick and camera to complete the design of intelligent inspection terminal. Then the author investigated the common types of road damage, including long strip cracks, reticulation cracks, potholes, and rutting. After that, an SSD-mobilenet architecture was modified and a database including a large number of images for different types of damage was built. The SSD-mobilenet was trained and validated with the built database. Transplanting the SSD-mobilenet to the intelligent inspection terminal, which could realize the road damage detection based on machine vision. The result shows 80.87% average precision (AP) ratings for different types of damage and proves the proposed method is effective.
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