An Automatic Detection Method of Bird’s Nest on Transmission Line Tower Based on Faster_RCNN

Abstract

The bird's nest on the transmission line tower has a bad impact on the transmission equipment, and even threaten the safe and stable operation of the power grid. In recent years, the number of bird pest in transmission line is increasing year by year, resulting in increasing economic losses. The traditional bird's nest identification method of transmission line is time-consuming and labor-intensive, and its security level is low. Therefore, this paper proposes an automatic detection method of bird's nest on transmission line tower based on Faster_RCNN convolution neural network. This method can automatically identify the location of the bird's nest on the transmission line tower by using the image collected by unmanned aerial vehicle (UAV). The problem of insufficient training samples and overfitting of neural network classifier is solved by enlarging the bird's nest image. The experimental results show that this method can effectively detect bird's nest targets in complex environment, and the highest recall rate can reach 95.38%, the highest F1 score can reach 96.87%, and the detection time of each image can reach 0.154s. Compared with the traditional nest detection method, this method has stronger applicability and generalization ability. It provides technical support for analyzing bird activities and taking effective preventive measures.

Survey of Security Protocols and Vulnerabilities in Unmanned Aerial Vehicles

“…It can be seen from Table 4, the accuracy of the work proposed in [72] is higher in comparison to other schemes. This means that the percentage of TP predictions for the scheme proposed in [72] is comparatively higher.…”

“…This means that the percentage of TP predictions for the scheme proposed in [72] is comparatively higher. However, the accuracy for [71] is slightly less than the scheme proposed in [72].…”

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“…Another approach that is currently being used is the detection of objects that have faults, such as broken insulators. Techniques such as the region-based convolutional neural network (R-CNN) presented by Li et al [35] and Li et al [36], and you only look once (YOLO) [37] are being widely used for insulator fault identification. The great advantage of this strategy is that it is possible to identify the exact location of the failure [38]; then, specialized teams can be directed to solve the problem by having its cause and location defined in advance.…”

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“…In this case, the recall values (r〗_i) and precision ( p(r〗_i) ) are implemented in the graph and then connected by orange lines. By using formula (11) the points P_interp (r_(i+1) ) are also connected by a green line. Furthermore, the AP value is obtained by calculating the area under the green curve.…”