Weather-Adaptive Synthetic Data Generation for Enhanced Power Line Inspection Using StarGAN
Accurately detecting power line defects under diverse weather conditions is crucial for ensuring power grid reliability and safety. Existing power line inspection datasets, while valuable, often lack the diversity needed for training robust machine learning models, particularly for adverse weather s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10807178/ |
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| Summary: | Accurately detecting power line defects under diverse weather conditions is crucial for ensuring power grid reliability and safety. Existing power line inspection datasets, while valuable, often lack the diversity needed for training robust machine learning models, particularly for adverse weather scenarios like fog, rain, and nighttime conditions. This paper addresses this limitation by introducing a novel framework for generating synthetic power line images under diverse weather conditions, thereby enhancing the diversity and robustness of power line inspection systems. The proposed approach employs a combination of novel heuristic image processing techniques and a multi-domain Generative Adversarial Network (GAN) called StarGAN-v2. Initial transformations using heuristic methods simulate rainy, foggy, and nighttime conditions, providing a foundation for the GAN to learn accurate mappings between weather domains. The StarGAN-v2 model, achieving its best performance with a latent dimension of 16, yielded a Frechet Inception Distance (FID) score of 24.72 and a Learned Perceptual Image Patch Similarity (LPIPS) score of 0.37 for fog, indicating high fidelity and perceptual similarity to real images. Furthermore, the impact of incorporating these synthetic images into the training process of various object detection models is thoroughly examined. The results show that models trained on a combination of synthetic and real data outperform those trained solely on either real data only or synthetic data only. |
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| ISSN: | 2169-3536 |