End-to-End Image Compression with Segmentation Guided Dual Coding for Wind Turbines
Summary: arXiv:2603.29927v1 Announce Type: cross
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.
Introduction
Wind turbine inspections are critical for maintaining the integrity and efficiency of renewable energy production. However, the high-resolution images generated during these inspections pose challenges in terms of data transfer and analysis. Traditional methods often struggle with balancing the quality of important regions, such as turbine blades, against the need for compressing less relevant background details. This article discusses a newly developed framework that addresses these issues by integrating segmentation and dual-mode compression techniques.
Proposed Framework
The proposed framework is designed to enhance the efficiency of image compression while ensuring that critical details necessary for defect detection are not compromised. The framework consists of three key components:
- Segmentation Network: Utilizing a robust segmentation network (BU-Netv2+P) with a Conditional Random Field (CRF)-regularized loss, the system accurately identifies the blade regions of the turbine.
- Hyperprior-based Autoencoder: This autoencoder is optimized specifically for lossy compression, allowing for the efficient encoding of blade images while retaining essential details.
- Bits-back Coder: An extended bits-back coder with hierarchical models is employed for fully lossless reconstruction of blade images. This innovative approach ensures that the most critical data is preserved without any loss.
Innovative Features
One of the standout features of this framework is its ability to remove the sequential dependency typically found in bits-back coding systems. By reusing background-coded bits, the framework enables a more parallelized and efficient dual-mode compression process. This advancement not only speeds up the coding process but also enhances overall performance.
Results and Performance
Experiments conducted on a large-scale wind turbine dataset have yielded promising results. The proposed framework demonstrates superior compression performance and efficiency compared to conventional methods. By optimizing the encoding process for both the blade areas and the background, the framework provides a practical solution for automated inspections, ensuring that defect detection capabilities remain intact.
Conclusion
In conclusion, the integration of segmentation and dual-mode compression in the proposed framework represents a significant advancement in the field of image processing for wind turbine inspections. By focusing on the critical blade regions while efficiently managing the background, this approach not only enhances the quality of defect detection but also streamlines the data transfer process, paving the way for more effective automated inspections in the renewable energy sector.