Abstract
In order to accurately detect the number of birds around the transmission line, promptly drive the birds away to ensure the normal operation of the line, a DC-YOLO model is designed. This model is based on the deep learning target detection algorithm YOLO V3 and proposes two improvements: Replacing the convolutional layer in the original network with dilated convolution to maintain a larger receptive field and higher resolution, improving the model’s accuracy for small targets; The confidence score of the detection frame is updated by calculating the scale factor, and the detection frame with a score lower than the threshold is finally removed. The NMS algorithm is optimized to improve the model’s ability to detect occluded birds. Experimental results show that the DC-YOLO model detection accuracy can reach 86.31%, which can effectively detect birds around transmission lines.
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Zou, C., Liang, Yq. (2020). Bird Detection on Transmission Lines Based on DC-YOLO Model. In: Shi, Z., Vadera, S., Chang, E. (eds) Intelligent Information Processing X. IIP 2020. IFIP Advances in Information and Communication Technology, vol 581. Springer, Cham. https://doi.org/10.1007/978-3-030-46931-3_21
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