Abstract
Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation and dual-mode (lossy and lossless) compression. The segmentation module accurately identifies the blade region, after which our region-of-interest (ROI) compressor encodes it at superior quality compared to the rest of the image. Unlike conventional ROI schemes that merely allocate more bits to salient areas, our framework integrates: (i) a robust segmentation network (BU-Netv2+P) with a CRF-regularized loss for precise blade localization, (ii) a hyperprior-based autoencoder optimized for lossy compression, and (iii) an extended bits-back coder with hierarchical models for fully lossless blade reconstruction. Furthermore, our ROI framework removes the sequential dependency in bits-back coding by reusing background-coded bits, enabling parallelized and efficient dual-mode compression. To the best of our knowledge, this is the first fully integrated learning-based ROI codec combining segmentation, lossy, and lossless compression, ensuring that subsequent defect detection is not compromised. Experiments on a large-scale wind turbine dataset demonstrate superior compression performance and efficiency, offering a practical solution for automated inspections.
Abstract (translated)
在风力涡轮机检测过程中传输大量高分辨率图像，会对评估和检测严重缺陷造成瓶颈。高效编码必须在叶片区域保持高保真度，同时高效压缩背景区域。本研究提出一个端到端深度学习框架，联合执行分割与双模式（有损和无损）压缩。该框架的分割模块能精确定位叶片区域，随后其感兴趣区域（ROI）压缩器以优于图像其他部分的质量对其进行编码。与传统ROI方案仅向显著区域分配更多比特不同，本框架整合了以下组件：（i）配备CRF正则化损失的鲁棒分割网络（BU-Netv2+P），用于精确叶片定位；（ii）针对有损压缩优化的基于超先验的自编码器；（iii）采用分层模型的扩展比特回传编码器，实现完全无损的叶片重建。此外，本ROI框架通过复用背景编码比特，消除了比特回传编码中的顺序依赖，实现了并行化高效双模式压缩。据我们所知，这是首个完全集成化的基于学习的ROI编解码器，结合了分割、有损与无损压缩，确保后续缺陷检测不受影响。在大规模风力涡轮机数据集上的实验表明，该方法在压缩性能和效率上均具有优势，为自动化检测提供了实用解决方案。
URL
https://arxiv.org/abs/2603.29927
