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Bird's Nest Detection in Power Transmission Lines with Fusion of Attention and Multi-scale Features

Published: 28 June 2024 Publication History

Abstract

To address the issues of low intelligence level and inefficient inspection of high-voltage transmission line patrols, this study combines computer vision technology to propose an improved YOLOv5-based detection algorithm, named YOLOv5_ACG, for detecting bird nests on high-voltage transmission lines. The algorithm addresses challenges such as significant scale variations, indistinct features, and the potential for false positives and false negatives.Firstly, a feature fusion mechanism is employed in the neck network to optimize the detection head, improving the feature fusion to handle the diverse scales of targets. Secondly, an enhanced learnable parallel weighted attention module is utilized to enhance the model's ability to focus on bird nest features. Lastly, lightweight convolutions are employed to reduce network parameters and computations, minimizing real-time performance losses resulting from increased model complexity.Experimental results demonstrate that YOLOv5_ACG achieves a 93.0% [email protected] on a self-made transmission line bird nest dataset, which is a 2.2% improvement over YOLOv5. This algorithm enhances the detection accuracy of bird nests on transmission lines in practical application scenarios, making it more suitable for embedded devices used in actual transmission line bird nest detection applications.

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  1. Bird's Nest Detection in Power Transmission Lines with Fusion of Attention and Multi-scale Features

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    ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
    September 2023
    335 pages
    ISBN:9798400708039
    DOI:10.1145/3655532
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Association for Computing Machinery

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    Published: 28 June 2024

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    Author Tags

    1. Adaptive feature fusion, Attention mechanism, Lightweightization
    2. Transmission line bird's nest, YOLOv5

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