Improved CNN Recognition Algorithm for Identifying Bird Hazards in Transmission Lines

With the expansion of the power grid, bird activities have become the main factor causing transmission line failures. How to accurately identify hazard birds has received widespread attention from all sectors of society. However, the current bird identification methods for transmission line hazards suffer from low accuracy due to the small size of bird targets. This study proposes an enhanced Convolutional Neural Network (CNN) with Support Vector Machines (SVM) to improve the accuracy of identifying hazardous birds on transmission lines. At the same time, a dataset of bird species affected by transmission lines is constructed, and data augmentation methods and denoising deep convolutional networks are used to process the data. Thus, a bird identification algorithm for transmission line hazards based on improved CNNs and SVM is constructed by combining the three. The study conducts a performance comparison analysis of the algorithm and finds that its average recognition speed and accuracy are 9.8 frames per second and 97.4%, respectively, significantly better than the compared algorithms. In addition, an analysis of the application effect of the algorithm is conducted, and it is found that the algorithm can accurately identify hazard birds. In some recognition results, the recognition results and confirmation probabilities for Pica Pica, ciconia boyciana, egretta garzetta, and hirundo rusticas are 98.73%, 97.68%, 96.54%, and 91.34%, respectively, all above 90%. The above findings indicate that the proposed identification algorithm has good performance and practical value, which helps to improve the accuracy of identifying hazard birds on transmission lines.

@article{Li2025,
  title     = {Improved CNN Recognition Algorithm for Identifying Bird Hazards in Transmission Lines},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {3},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Junzhou Li and Yao Li and Wen Wang},
  doi       = {10.14569/IJACSA.2025.0160325},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160325}
}