Wind Turbine Feature Detection Using Deep Learning and Synthetic Data

• URL: http://arxiv.org/abs/2507.21611v1
• Date: Tue, 29 Jul 2025 09:06:44 GMT
• Title: Wind Turbine Feature Detection Using Deep Learning and Synthetic Data
• Authors: Arash Shahirpour, Jakob Gebler, Manuel Sanders, Tim Reuscher,
• Abstract summary: We propose a method to generate synthetic training data that allows controlled variation of visual and environmental factors.<n>We train a YOLOv11 feature detection network solely on synthetic WT images with a modified loss function.<n>The resulting network is evaluated both using synthetic images and a set of real-world WT images and shows promising performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: For the autonomous drone-based inspection of wind turbine (WT) blades, accurate detection of the WT and its key features is essential for safe drone positioning and collision avoidance. Existing deep learning methods typically rely on manually labeled real-world images, which limits both the quantity and the diversity of training datasets in terms of weather conditions, lighting, turbine types, and image complexity. In this paper, we propose a method to generate synthetic training data that allows controlled variation of visual and environmental factors, increasing the diversity and hence creating challenging learning scenarios. Furthermore, we train a YOLOv11 feature detection network solely on synthetic WT images with a modified loss function, to detect WTs and their key features within an image. The resulting network is evaluated both using synthetic images and a set of real-world WT images and shows promising performance across both synthetic and real-world data, achieving a Pose mAP50-95 of 0.97 on real images never seen during training.

Related papers

• Pseudo-Label Guided Real-World Image De-weathering: A Learning Framework with Imperfect Supervision [57.5699142476311]
  We propose a unified solution for real-world image de-weathering with non-ideal supervision.<n>Our method exhibits significant advantages when trained on imperfectly aligned de-weathering datasets.
  arXiv  Detail & Related papers  (2025-04-14T07:24:03Z)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models.<n>In this paper, we investigate how detection performance varies across model backbones, types, and datasets.<n>We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• WTCL-Dehaze: Rethinking Real-world Image Dehazing via Wavelet Transform and Contrastive Learning [17.129068060454255]
  Single image dehazing is essential for applications such as autonomous driving and surveillance. We propose an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform. Our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods.
  arXiv  Detail & Related papers  (2024-10-07T05:36:11Z)
• Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective [45.210030086193775]
  Current synthetic image detection (SID) pipelines are primarily dedicated to crafting universal artifact features.<n>We propose SAFE, a lightweight and effective detector with three simple image transformations.<n>Our pipeline achieves a new state-of-the-art performance, with remarkable improvements of 4.5% in accuracy and 2.9% in average precision against existing methods.
  arXiv  Detail & Related papers  (2024-08-13T09:01:12Z)
• Training Deep Learning Models with Hybrid Datasets for Robust Automatic Target Detection on real SAR images [0.13194391758295113]
  We propose a Deep Learning approach to train ATD models with synthetic target signatures produced with the MOCEM simulator. We train ATD models specifically tailored to bridge the domain gap between synthetic and real data. Our approach can reach up to 90% of Average Precision on real data while exclusively using synthetic targets for training.
  arXiv  Detail & Related papers  (2024-05-15T09:26:24Z)
• SynFog: A Photo-realistic Synthetic Fog Dataset based on End-to-end Imaging Simulation for Advancing Real-World Defogging in Autonomous Driving [48.27575423606407]
  We introduce an end-to-end simulation pipeline designed to generate photo-realistic foggy images. We present a new synthetic fog dataset named SynFog, which features both sky light and active lighting conditions. Experimental results demonstrate that models trained on SynFog exhibit superior performance in visual perception and detection accuracy.
  arXiv  Detail & Related papers  (2024-03-25T18:32:41Z)
• Improving Lens Flare Removal with General Purpose Pipeline and Multiple Light Sources Recovery [69.71080926778413]
  flare artifacts can affect image visual quality and downstream computer vision tasks. Current methods do not consider automatic exposure and tone mapping in image signal processing pipeline. We propose a solution to improve the performance of lens flare removal by revisiting the ISP and design a more reliable light sources recovery strategy.
  arXiv  Detail & Related papers  (2023-08-31T04:58:17Z)
• Physics-Driven Turbulence Image Restoration with Stochastic Refinement [80.79900297089176]
  Image distortion by atmospheric turbulence is a critical problem in long-range optical imaging systems. Fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions. This paper proposes the Physics-integrated Restoration Network (PiRN) to help the network to disentangle theity from the degradation and the underlying image.
  arXiv  Detail & Related papers  (2023-07-20T05:49:21Z)
• PennSyn2Real: Training Object Recognition Models without Human Labeling [12.923677573437699]
  We propose PennSyn2Real - a synthetic dataset consisting of more than 100,000 4K images of more than 20 types of micro aerial vehicles (MAVs) The dataset can be used to generate arbitrary numbers of training images for high-level computer vision tasks such as MAV detection and classification. We show that synthetic data generated using this framework can be directly used to train CNN models for common object recognition tasks such as detection and segmentation.
  arXiv  Detail & Related papers  (2020-09-22T02:53:40Z)
• Syn2Real Transfer Learning for Image Deraining using Gaussian Processes [92.15895515035795]
  CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. Due to challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset.
  arXiv  Detail & Related papers  (2020-06-10T00:33:18Z)
• Can Synthetic Data Improve Object Detection Results for Remote Sensing Images? [15.466412729455874]
  We propose the use of realistic synthetic data with a wide distribution to improve the performance of remote sensing image aircraft detection. We randomly set the parameters during rendering, such as the size of the instance and the class of background images. In order to make the synthetic images more realistic, we refine the synthetic images at the pixel level using CycleGAN with real unlabeled images.
  arXiv  Detail & Related papers  (2020-06-09T02:23:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.