Captured Image Data Research Articles

Classification Model of Grassland Desertification Based on Deep Learning

Grasslands are one of the most important ecosystems on earth, and the impact of grassland desertification on the earth’s environment and ecosystem cannot be ignored. Accurately distinguishing grassland desertification types has important application value. The appropriate grazing strategies can be implemented based on these distinctions. Grassland conservation measures can be tailored accordingly. This contributes to further protecting and restoring grassland vegetation. This project takes color images labeled with the desertification types of grasslands as the research object, uses the currently popular deep learning model as the classification tool, and then establishes a color image-based grassland desertification classification model based on the feature extraction network, based on the Vision Transformer model, by comparing the various deep learning image classification models. The experimental results show that, despite the complex structure and large number of parameters of the grassland desertification classification model obtained in this project, the test accuracy rate reaches 88.72% and the training loss is only 0.0319. Compared with the popular classification models such as VGG16, ResNet50, ResNet101, DenseNet101, DenseNet169, and DenseNet201, and so on, the Vision Transformer demonstrates clear advantages in classification accuracy, fitting ability, and generalization capacity. By integrating with deep learning technology, the model can be applied to grassland management and ecological restoration. Mobile devices can be used to conveniently capture image data, and information can be processed quickly. This provides efficient tools for grazing managers, environmental scientists, and conservation organizations. These tools assist in quickly assessing the extent of grassland desertification, optimizing grassland management and conservation decisions. Furthermore, strong technical support is offered for the ecological restoration and sustainable management of desertification grasslands.

AnnotateXR: An Extended Reality Workflow for Automating Data Annotation to Support Computer Vision Applications

Abstract Computer vision (CV) algorithms require large annotated datasets that are often labor-intensive and expensive to create. We propose AnnotateXR, an extended reality (XR) workflow to collect various high-fidelity data and auto-annotate it in a single demonstration. AnnotateXR allows users to align virtual models over physical objects, tracked with six degrees-of-freedom (6DOF) sensors. AnnotateXR utilizes a hand tracking capable XR head-mounted display coupled with 6DOF information and collision detection to enable algorithmic segmentation of different actions in videos through its digital twin. The virtual–physical mapping provides a tight bounding volume to generate semantic segmentation masks for the captured image data. Alongside supporting object and action segmentation, we also support other dimensions of annotation required by modern CV, such as human–object, object–object, and rich 3D recordings, all with a single demonstration. Our user study shows AnnotateXR produced over 112,000 annotated data points in 67 min.

Deep learning-assisted multispectral imaging for early screening of skin diseases

IntroductionMelanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This process is labor-intensive and resource-consuming. Traditional methods for diagnosing skin lesions rely heavily on the subjective judgment of dermatologists, leading to issues in diagnostic accuracy and prolonged detection times. ObjectivesThis study aims to introduce a multispectral imaging (MSI)-based method for the early screening and detection of skin surface lesions. By capturing image data at multiple wavelengths, MSI can detect subtle spectral variations in tissues, significantly enhancing the differentiation of various skin conditions. MethodsThe proposed method utilizes a pixel-level mosaic imaging spectrometer to capture multispectral images of lesions, followed by reflectance calibration and standardization. Regions of interest were manually extracted, and the spectral data were subsequently exported for analysis. An improved one-dimensional convolutional neural network is then employed to train and classify the data. ResultsThe new method achieves an accuracy of 96.82 % on the test set, demonstrating its efficacy. ConclusionThis multispectral imaging approach provides a non-contact and non-invasive method for early screening, effectively addressing the subjective identification of lesions by dermatologists and the prolonged detection times associated with conventional methods. It offers enhanced diagnostic accuracy for a variety of skin lesions, suggesting new avenues for dermatological diagnostics.

A comprehensive approach to detecting chemical adulteration in fruits using computer vision, deep learning, and chemical sensors

Contamination of harmful additives in fruits has become a concerning norm these days. Owing to the great popularity of fruits, dishonest vendors frequently use harmful chemicals to contaminate fruits to extend their shelf life, which is extremely dangerous for the general public's health. To mitigate this issue, machine-learning algorithms like Decision Tree Classifier, Naïve Bayes and a deep learning model named “DurbeenNet” are evaluated separately. Alongside, a computer vision-based detection method coupled with a hybrid model is proposed that combines deep learning and chemical sensor. Formaldehyde Detection Sensor is used in this experiment to take reading of the sensor data. Mango, Apple, Banana, and Malta are taken as sample fruits in this study. Sensor data for both fresh and chemical-mixed fruit is newly collected using Formaldehyde Detection Sensor. The above mentioned sensor data along with the previously captures images of both fresh and chemical-mixed state are being integrated to a hybrid model. Among two machine learning algorithms naïve bayes come up with 82 % accuracy. Using both sensor data and captured image data, the proposed model “SensorNet” provides highest accuracy of 97.03 % which is substantial than “DurbeenNet” model's accuracy. Through the utilization of these fruit samples, formaldehyde detection sensor provides instantaneous detection, identifying the specific toxic substances present in the contaminated fruits.

Low-light wheat image enhancement using an explicit inter-channel sparse transformer

Low-light image enhancement poses a significant challenge in agricultural settings, particularly for time-series captured wheat images. The use of time-series captured image data can improve the accuracy of wheat yield prediction by analyzing the growth status and characteristics of wheat. Given the difficulty of capturing paired wheat images in various lighting conditions, a method based on the Global Wheat Head Detection (GWHD) dataset is presented to synthesize pairs of low-light/normal-light wheat images. This approach expands to create a new dataset, LN-GWHD, specifically for the task of enhancing wheat images in low-light conditions. Importantly, it enables a more extensive evaluation of wheat image restoration tasks and performance accuracy under more controlled and diverse conditions. It is worth noting that this method can be extended to a wide range of scenarios for the enhancement of low-light situations. Where low-light conditions are common due to various factors such as weather, existing methods based on the transformer architecture have achieved state-of-the-art (SOTA) enhancement performance by capturing relationships between low-light feature channels. However, these approaches perform the similarity computation on all tokens and may introduce redundant features, limiting their ability to focus on critical information for high-quality image reconstruction. To recover more high-quality wheat image information, the explicit inter-channel sparse transformer (EIST) network is proposed, which is specifically designed to recover wheat images under low-light conditions. EIST comprises multiple blocks, each featuring explicit sparse top-k attention (ESTA) and bilateral gated feed-forward network (BGFN). ESTA dynamically selects the most critical information by focusing on the top-k key labels in each query, while BGFN enhances valid information interaction through re-double filtering. The overall architecture employs transformers to learn correlations and features between different image channels, resulting in noise reduction, detail enhancement, and improved clarity. Additionally, the Fourier spectrum loss is introduced to constrain the image reconstruction in the frequency domain, allowing more detailed information to be recovered. Extensive experiments on the LN-GWHD datasets demonstrate that EIST surpasses SOTA methods. Furthermore, our approach achieves superior results in detecting low-light wheat ears during back-end evaluation.

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

Effect of an image compression scheme on optical camera communication

Optical camera communication (OCC) represents a subset of visible light communication with considerable potential applications in domains such as intelligent transportation systems. However, many related studies on OCC ignore the effect of image compression. Cameras typically compress captured image data to reduce the resources required to store the data. Taking Joint Photographic Experts Group (JPEG) and AV1 image file format (AVIF) compression as examples, the effect is studied based on a simplified OCC model. The simulation results indicate that image compression can achieve the effect of a low-pass filter. Image compression, especially AVIF compression, can effectively reduce the bit error ratio (BER) of the system. Compared with demodulating RAW images, the BER is reduced from 1.2×10−1 to 7.0×10−4 when the AVIF image compression ratio is approximately 140. In addition, image compression is one of the reasons for the different performance of the red, green, and blue channels.

Onboard Data Prioritization Using Multi-Class Image Segmentation for Nanosatellites

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing.

Digital twin of multi-model drone detection system on Airsim for RF and vision modalities

Drones have become more prevalent in recent years and are used for both beneficial and malicious purposes. As a result, protecting restricted areas from unauthorized drone activities has become crucial. However, some researchers face challenges in developing drone detection systems due to the high costs of necessary equipment. This paper presents an innovative solution by creating an Airsim Graphical User Interface (GUI) tool compatible with the Unreal Engine. This tool enables the simulation of drone flights and creation of image and radio frequency (RF) datasets for drone detection in a simulation environment. Our approach involves modeling the measurement devices such as cameras to capture image data and software defined radio (SDR) receiver to capture RF signals as raw in-phase and quadrature (IQ) data. Moreover, users can manage automated route planning for drones, recording configurations, and different cameras and RF configurations. Researchers can now generate datasets with various images and RF configurations without the need for physical drones, cameras, or SDRs, enabling experimentation with different drone detection models. Furthermore, we proposed models for drone detection systems by using generated datasets from the proposed dataset generation system.

Yield prediction in a peanut breeding program using remote sensing data and machine learning algorithms.

Peanut is a critical food crop worldwide, and the development of high-throughput phenotyping techniques is essential for enhancing the crop's genetic gain rate. Given the obvious challenges of directly estimating peanut yields through remote sensing, an approach that utilizes above-ground phenotypes to estimate underground yield is necessary. To that end, this study leveraged unmanned aerial vehicles (UAVs) for high-throughput phenotyping of surface traits in peanut. Using a diverse set of peanut germplasm planted in 2021 and 2022, UAV flight missions were repeatedly conducted to capture image data that were used to construct high-resolution multitemporal sigmoidal growth curves based on apparent characteristics, such as canopy cover and canopy height. Latent phenotypes extracted from these growth curves and their first derivatives informed the development of advanced machine learning models, specifically random forest and eXtreme Gradient Boosting (XGBoost), to estimate yield in the peanut plots. The random forest model exhibited exceptional predictive accuracy (R2 = 0.93), while XGBoost was also reasonably effective (R2 = 0.88). When using confusion matrices to evaluate the classification abilities of each model, the two models proved valuable in a breeding pipeline, particularly for filtering out underperforming genotypes. In addition, the random forest model excelled in identifying top-performing material while minimizing Type I and Type II errors. Overall, these findings underscore the potential of machine learning models, especially random forests and XGBoost, in predicting peanut yield and improving the efficiency of peanut breeding programs.

Identification of traditional settlement landscape areas in Hainan based on unmanned aerial remote sensing imagery

Abstract To enhance the accuracy of recognizing traditional settlement landscapes in Hainan, this study introduces a landscape recognition model predicated on a full convolutional neural network (FCN). The research delineates the selection of specific types and sensors for Unmanned Aerial Vehicles (UAVs), employing UAV remote sensing technology to capture image data of Hainan's traditional settlement landscapes. The collected initial image data, laden with interference information, underwent a preprocessing step. On this foundation, the landscape recognition model for Hainan's traditional settlements was developed utilizing the FCN. The empirical results derived from deploying the model reveal the presence of ancient buildings within a 400-meter radius of the principal river. Notably, these structures predominantly span a distance ranging from 100 to 350 meters. The spatial distribution pattern of these ancient edifices notably centers around the Zong ancestral hall. Furthermore, when compared to other benchmark models, the proposed FCN model exhibits superior performance in recognizing forest, grassland, and farmland within the Hainan settlement landscape, achieving average recognition rates of 88.66% and 84.91%, respectively. This investigation underscores the significant potential for applying UAV remote sensing technology in identifying traditional settlement landscapes. It provides pivotal technical support and a reference point for the survey of forest resources and ecological monitoring, thereby enhancing the applicability and dissemination of UAV technology in landscape recognition tasks.

An Improved MobileNetV3 Mushroom Quality Classification Model Using Images with Complex Backgrounds

Shiitake mushrooms are an important edible fungus, and their nutrient content is related to their quality. With the acceleration of urbanization, there has been a serious loss of population and shortage of labor in rural areas. The problem of harvesting agricultural products after maturity is becoming more and more prominent. In recent years, deep learning techniques have performed well in classification tasks using image data. These techniques can replace the manual labor needed to classify the quality of shiitake mushrooms quickly and accurately. Therefore, in this paper, a MobileNetV3_large deep convolutional network is improved, and a mushroom quality classification model using images with complex backgrounds is proposed. First, captured image data of shiitake mushrooms are divided into three categories based on the appearance characteristics related to shiitake quality. By constructing a hybrid data set, the model’s focus on shiitake mushrooms in images with complex backgrounds is improved. And the constructed data set is expanded using data enhancement methods to improve the generalization ability of the model. The total number of images after expansion is 10,991. Among them, the number of primary mushroom images is 3758, the number of secondary mushroom images is 3678, and the number of tertiary mushroom images is 3555. Subsequently, the SE module in MobileNetV3_large network is improved and processed to enhance the model recognition accuracy while reducing the network size. Finally, PolyFocalLoss and migration learning strategies are introduced to train the model and accelerate model convergence. In this paper, the recognition performance of the improved MobileNetV3_large model is evaluated by using the confusion matrix evaluation tool. It is also compared with other deep convolutional network models such as VGG16, GoogLeNet, ResNet50, MobileNet, ShuffleNet, and EfficientNet using the same experimental conditions. The results show that the improved MobileNetV3_large network has a recognition accuracy of 99.91%, a model size of 11.9 M, and a recognition error rate of 0.09% by the above methods. Compared to the original model, the recognition accuracy of the improved model is increased by 18.81% and the size is reduced by 26.54%. The improved MobileNetV3_large network model in this paper has better comprehensive performance, and it can provide a reference for the development of quality recognition and classification technologies for shiitake mushrooms cultivated in greenhouse environments.

Research on a random sampling method for bulk grain based on the M-Unet and SGBM algorithms

In response to the challenge of limited real-time capability in identifying and positioning sampling points during random sampling of bulk grains in industrial settings, this paper introduces a novel approach. It combines the utilization of a lightweight neural network model, known as M-Unet, with the Semi-Global Block Matching (SGBM) algorithm to achieve the random sampling of bulk grains. The process commences with the deployment of a binocular camera to capture image data from the grain loading area, followed by binocular correction. Recognizing the constraints associated with the traditional U-Net network model, characterized by large parameters and suboptimal real-time performance, we introduce the M-Unet network, which is a more efficient alternative. M-Unet incorporates MobileNet as a substitute for the U-Net network encoder, resulting in a significant improvement in real-time performance and segmentation quality. Subsequently, the M-Unet network is employed to partition the corrected left image into multiple regions based on predefined sampling principles, and random sampling points are generated within each region. To determine the three-dimensional spatial positioning of these sampling points, we employ the SGBM algorithm, a semi-global stereo matching technique, on the left and right images of the corrected grain loading area. Experimental results demonstrate that the proposed method exhibits robust real-time performance and effectively recognizes grain loading areas. Furthermore, this method is suitable for deployment on portable devices, offering novel insights and methodologies for automating and advancing the intelligence of grain sampling processes. This innovation effectively addresses the challenges associated with low efficiency and human-related factors in traditional grain sampling. The findings presented in this paper carry substantial significance in safeguarding the interests of grain farmers, reducing food losses, minimizing waste, and enhancing the intelligence of sampling processes.

Littering Activities Monitoring using Image Processing

Littering is a human behavior that become a habit since childhood. Even though there are rules that prohibit this behavior, the community still continues to do so. In order to limit this bad behavior, a device that can monitor and provide notifications is needed. In this research, a device is offered that can identify human activities in real time using webcam-based image processing. Then, it is processed by machine learning using the Recurrent Neural Network (RNN). The monitoring device produced in this research works by comparing the captured image data with a dataset. The captured image data will then be extracted features and form several coordinate points on the human body, then the system will classify these human activities into the category of normal activities or littering activities. This device will provide an output in the form of a warning every time the activity of littering is detected.

Acquisition and usage of robotic surgical data for machine learning analysis

BackgroundThe increasing use of robot-assisted surgery (RAS) has led to the need for new methods of assessing whether new surgeons are qualified to perform RAS, without the resource-demanding process of having expert surgeons do the assessment.Computer-based automation and artificial intelligence (AI) are seen as promising alternatives to expert-based surgical assessment. However, no standard protocols or methods for preparing data and implementing AI are available for clinicians. This may be among the reasons for the impediment to the use of AI in the clinical setting.MethodWe tested our method on porcine models with both the da Vinci Si and the da Vinci Xi. We sought to capture raw video data from the surgical robots and 3D movement data from the surgeons and prepared the data for the use in AI by a structured guide to acquire and prepare video data using the following steps: ‘Capturing image data from the surgical robot’, ‘Extracting event data’, ‘Capturing movement data of the surgeon’, ‘Annotation of image data’.Results15 participant (11 novices and 4 experienced) performed 10 different intraabdominal RAS procedures. Using this method we captured 188 videos (94 from the surgical robot, and 94 corresponding movement videos of the surgeons’ arms and hands). Event data, movement data, and labels were extracted from the raw material and prepared for use in AI.ConclusionWith our described methods, we could collect, prepare, and annotate images, events, and motion data from surgical robotic systems in preparation for its use in AI.

An Intelligent Collaborative Image-Sensing System for Disease Detection

With the growth of smart medical devices and applications in smart hospitals, home care facilities, nursing, and the Internet of Medical Things (IoMT) are becoming more ubiquitous. It uses smart medical devices and cloud computing services, and basic Internet of Things (IoT) technology, to detect key body indicators, monitor health situations, and generate multivariate data to provide just-in-time healthcare services. In this article, we present a novel collaborative disease detection system based on IoMT amalgamated with captured image data. The system can be based on intelligent agents, where every agent explores the interaction between different medical data obtained by smart sensor devices using reinforcement learning as well as targets to detect diseases. The agents then collaborate to make a reliable conclusion about the detected diseases. Intensive experiments were conducted using medical data. The results show the importance of using intelligent agents for disease detection in healthcare decision-making. Moreover, collaboration increases the detection rate, with numerical results showing the superiority of the proposed framework compared with baseline solutions for disease detection.

Development of a Miniature Embedded Imaging System For Internal Flow Measurements

Through this work, the foundation has been laid for the development of a system for small-scale, integrated, image- based flow diagnostics for ground and flight tests. This system addresses many of the challenges associated with alternative methods of performing these measurements. For this work a 120 fps imager was embedded in the ceiling of a Mach 2.0 wind tunnel to perform oblique shock impingement measurements. Illumination was provided via a ring-light system of micro-LEDs embedded around the camera lens aperture. The entire system was controlled using a Raspberry Pi 4 Model B to capture and process image data. The integrated system possesses a total aperture diameter of 20 mm. Pressure sensitive paint and UV oil flow visualization experiments capturing an oblique shock/boundary layer interaction were performed using this system.

Sports Biology Seminar of Three-dimensional Movement Characteristics of Yoga Standing Based on Image Recognition

With the exponential improvement of the integration capabilities of electronic hardware devices, digital images have become an indispensable information carrier, thus promoting the development of image recognition, detection, or tracking technologies. At present, there is a blank in the field of image recognition measured by the action standard of yoga standing three-dimensional movement, which will bring huge room for researchers to play. Based on the traditional image recognition technology, there are cases where missed detection or false detection cannot be correctly identified and made a correct judgment. In this paper, the ability to capture the action is improved by optimizing the network for the positioning of the stereoscopic action target of the yoga station. The linear discriminant analysis (LDA) after adding optimization is used to reduce the dimensionality of the captured image data, which is beneficial to improving image recognition rate and reducing image loss rate. Through the analysis of the three-dimensional movement of the yoga station, the model based on the improved algorithm is compared with the traditional model, and the final image recognition accuracy is improved compared with the network model before the improvement. The image recognition error rate steadily tends to 5%, and the loss rate is also below 2%. Through the optimized convolutional neural network, this paper can accurately capture the image position, and the recognition rate has also been greatly improved, which can provide a reference for future research in other fields.

Earth Observation Mission of a 6U CubeSat with a 5-Meter Resolution for Wildfire Image Classification Using Convolution Neural Network Approach

The KITSUNE satellite is a 6-unit CubeSat platform with the main mission of 5-m-class Earth observation in low Earth orbit (LEO), and the payload is developed with a 31.4 MP commercial off-the-shelf sensor, customized optics, and a camera controller board. Even though the payload is designed for Earth observation and to capture man-made patterns on the ground as the main mission, a secondary mission is planned for the classification of wildfire images by the convolution neural network (CNN) approach. Therefore, KITSUNE will be the first CubeSat to employ CNN to classify wildfire images in LEO. In this study, a deep-learning approach is utilized onboard the satellite in order to reduce the downlink data by pre-processing instead of the traditional method of performing the image processing at the ground station. The pre-trained CNN models generated in Colab are saved in RPi CM3+, in which, an uplink command will execute the image classification algorithm and append the results on the captured image data. The on-ground testing indicated that it could achieve an overall accuracy of 98% and an F1 score of a 97% success rate in classifying the wildfire events running on the satellite system using the MiniVGGNet network. Meanwhile, the LeNet and ShallowNet models were also compared and implemented on the CubeSat with 95% and 92% F1 scores, respectively. Overall, this study demonstrated the capability of small satellites to perform CNN onboard in orbit. Finally, the KITSUNE satellite is deployed from ISS on March 2022.

Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems

With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12599-022-00745-z.