End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines

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.

Figures (27)

• Figure 1: Overview of our ROI algorithm for blade image compression. Our method begins by extracting a segmentation mask using our proposed BU-Netv2+P. This mask is converted into a polygonal representation by classifying image patches as either blade (ROI) or background. The polygon mask is then efficiently encoded via its corner points. The background is highly compressed with our lossy coder, while the blade region is coded either lossily --in better quality and less distortion-- or losslessly. In ROI lossless case, the background is first encoded to provide its lossy bitstream for parallel bits-back coding.
• Figure 1: Four example instances of the blade imagery dataset. The complexity on this data arises due to the background variation and wild illumination conditions.
• Figure 2: BU-Netv2+P algorithm pipeline to segment blades: 1) an enhanced BU-Netv2, 2) a hole filling step, 3) a random forest module that exploits the input image set and the preliminary solution and, 4) a repeated hole filling operation.
• Figure 2: Validation accuracy after applying the random forest step with distinct sets of input pixels. The performance is analyzed in terms of the number of local pixel neighbors taken as input $n$ and the distance $d$ between these input pixels. The purple horizontal line represents the accuracy obtained before applying the random forest step.
• Figure 3: Hole filling scheme. Example of artifact removal inside the segmented blade region ($\mathbf{\hat{s}}^{BU}$). The method detects blade orientation to locate border edges (in red) and applies a standard hole-filling algorithm fill to produce the mask $\mathbf{\hat{s}}^{H}$.