PLAMD: Power Line Aerial Multi-Degradation Dataset
Abstract
## 📖 Overview**PLAMD (Power Line Aerial Multi-Degradation Dataset)** is a large-scale synthetic dataset designed for benchmarking image restoration and enhancement algorithms in the context of **unmanned aerial vehicle (UAV) power line inspection**. It covers **seven realistic degradation scenarios** with paired clean references, supporting full-reference evaluation paradigms.Source images are drawn from two public UAV power line inspection datasets — **InsPLAD** [1](https://doi.org/10.1080/01431161.2023.2283900) and **TTPLA** [2](https://arxiv.org/abs/2311.01619) — ensuring strong domain relevance for the power industry. Each image is then synthetically degraded using physically motivated or learning-based generation pipelines. ## Degradation Types | Type | Synthesis Method | Description || Haze | Atmospheric Scattering Model + Monodepth2 depth estimation | Fog/haze at various concentrations || Rain | Streak-based rain generation | Rainfall with tunable density, length, angle, and opacity || Snow | AutoSnow framework (5 snow conditions) | Streaking snow, fine snow, fluffy snow, and light ice accumulation || Motion Blur | Camera Shake Blur (CSB) generator | Physics-based random camera shake trajectory synthesis || Low-Light | Gamma correction + Poisson-Gaussian noise | Low-illumination evening/dusk scene simulation || Noise | sRGB real noise synthesizing (signal-dependent + signal-independent) | Realistic camera sensor noise || Inpainting | OpenCV-based random mask…
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