In recent years, due to the shortage of transmission line channel resources, power companies have begun to expand the capacity of existing important transmission lines. The safe and stable operation of important transmission lines is critical to the power supply status of users. Timely monitoring of potential hazards of construction machinery near the transmission lines has become a hot topic of transmission line protection against external damage. Aiming at the technical bottleneck of high false alarm rate of the current transmission line using online monitoring camera, this paper proposes a construction vehicle detection method based on the fusion of radar and visual features, which uses the physical features and geometric features of the target. The physical features such as velocity and acceleration are selected from radar. After the fusion of the radar data and camera data, the region of interest (ROI) of the radar target on the image is obtained, and the gradient direction histogram feature is extracted on the ROI. The visual features are calculated by the statistical features of gradient direction histogram, including standard deviation, median and average. This paper constructs a neural network R-V-DenseNet whose input is the fusion feature of radar and vision. Then a data set is made to train the network. The experimental results on the test set prove that the accuracy of R-V-DenseNet is improved compared with the traditional HOG-SVM method and the single sensor based detection method, which means the proposed method gains more accurate detection.
KEYWORDS: Video, Data modeling, Performance modeling, Image enhancement, Video processing, Safety, Feature extraction, Inspection, Control systems, Algorithm development
A method for predicting abnormal behaviors of substation workers based on video scenes and using generative confrontation networks to integrate global and local information is proposed. In the substation, this method can be used to issue timely warnings to the transportation and inspection personnel that may trigger dangerous actions during the operation, so as to provide an important guarantee for the life and safety of the transportation and inspection personnel. The human behavior prediction task aims to predict future behavior video frames based on a given behavior video frame. Considering that the human behavior video contains not only relatively stable scene information, but also time-varying and complex human behavior information, this method first uses a global generation confrontation network to generate video scenes and rough human contours; then uses local generation confrontation Network to further optimize the details of human behavior in the video. Experiments show that, compared with the existing methods that only use a single model to achieve pixel-level behavior prediction, the method of combining global and local generation proposed in this paper can better capture the spatial appearance and the timing dynamics of humans in the video.
With the rapid construction of UHV transmission lines, the geological disasters along these lines are increasing, which
seriously threatens the security and stability of lines and thus imperils the intrinsic safety of power network. At present,
there are still insufficient researches on the safety prevention of landslides in the tower foundations of transmission line.
On the basis of the existing researches of slope safety protection technology, the early protection technology for tower
foundation landslide under different engineering stages (planning and design stage, construction stage, operation and
maintenance stage) and different disaster modes (retrogressive type, trailing edge loading type, mixed type) are discussed
and analyzed. At the same time, from the three time scales of pre-disaster protection, immediate response and post-disaster
management, this paper systematically summarizes engineering protection measures of landslide in tower foundation.
Based on the research of this paper, it can provide effective support for the safety protection of landslide in tower
foundation.
With the rapic developments in most China cities, urban environment monitoring is very important, for example, for the subway safety, illegal drilling and construction in subway field should be quickly detected. Monitoring techniques with high precision and efficiency are vital to prevent the accidents and reduce losses, and UAV has great potential and advantages to conduct such task compared to human daily inspection. To quickly get the illegal operation information from UAV image, a method of change detection based on elevation difference and local binary pattern (LBP) is proposed to monitor the land surface along subway by unmanned aerial vehicle remote sensing (UAVRS). After accurate registrations without GCPs complete, the two DSMs and DOMS are mutually matched by the same points in their own model. Gaussian smoothing is used in gray-scale map to eliminating noise jamming before making change detection based on elevation difference. Pix4D is used to generate 2 DSMs of the study area, and texture feature is measured by LBP which is advanced in its rotational invariance and brightness invariance. Comparing with former researches, two DSMs are matched by invariant points in their own models instead of GCPs which are usually collected by GPS, with the registration precision less than 0.1m both in XY and Z directions, which meets the requirement of illegal operation detection in subway safety monitoring, and the adoption of LBP works well for images collected in different climate and illumination.
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