Harnessing Energy Datasets: Revolutionizing Infrastructure with Computer Vision
According to the International Trade Association, the UK generates 2.4GW/2.6GWh of operational energy storage across 161 sites, with an additional 20.2 GW in planning. While this seems to put the UK in the top 5 global energy producers, there is still room for growth. The vast network of energy infrastructure systems that transport energy from generation plants to homes and industries plays a vital role in the energy generation of any country. The energy infrastructure must support the energy being generated and distributed; otherwise, there would be a mass waste of energy. By leveraging computer vision, the energy industry can optimize existing energy infrastructure for better energy distribution, real-time fault detection, and remote monitoring of infrastructural systems.
This article will discuss the various energy datasets that computer vision engineers can use to exponentially optimize the current state of energy infrastructure in the energy industry, as well as some of the challenges facing the use of AI in the energy infrastructure industry and how to overcome them.
Computer Vision Solutions for the Energy Industry
Without easy access to the relevant data in the energy industry, it is hard to design distinctive and innovative solutions that use computer vision. To develop scalable and optimal solutions that can address the unique challenges of the energy business, computer vision models require specialized image datasets to perform properly. Computer vision solutions are numerous, especially as some are use-case-specific. Some existing applications of computer vision in the energy infrastructure industry are:
- Fault Detection and Predictive Maintenance: Faults occur in energy infrastructure due to severe weather, vegetation encroachment, accidental damage, animal interaction, etc. If left unattended, these faults could lead to bigger problems, such as explosions and environmental degradation. Computer vision models can be integrated into CCTV cameras to monitor the state of energy infrastructure and raise alarms when required. Sensors and cameras can also be used to collect data on trends or occurrences that cause faults in the infrastructure.
- Remote Monitoring: Instead of spending a lot of money on paying qualified personnel to monitor energy infrastructure manually, the government and other energy firms can save money by building a computer vision model to monitor the state of energy infrastructure. Remote monitoring increases the efficiency of infrastructural monitoring and gives qualified personnel more time to perform other important tasks.
- Environmental Sustainability Maintenance: The environment plays an important role in sustaining energy infrastructure. Before any infrastructure is set up, an environmental survey has to be performed to determine the suitability of the proposed location. Due to climate change, the environment is constantly being degraded, and this can cause a previously cleared site to become bad over time. Computer vision models can also help to inspect the environment's state routinely. In the event that the environment can no longer support the energy infrastructure, the power distribution plant is shut down before a larger environmental hazard occurs.
Common Energy Datasets
Here are some valuable energy datasets you can use to build powerful computer vision models that can optimize the performance of existing energy infrastructure or monitor their performance in the energy industry.
DTU - Drone inspection images of wind turbine
The DTU energy dataset consists of 701 high-resolution, temporal inspection images of ‘Nordtank’ (a wind turbine located at DTU Wind Energy’s test site in Denmark, Europe) taken between 2017 and 2018. The dataset consists of turbines with damaged or mounted objects such as VG panels, LE erosion, cracks, lightning receptors, etc. Damages like this reduce the turbines' speed and power generation efficiency and are thus undesirable.
Machine learning engineers can use the DTU dataset to create anomaly detection models that can identify these flaws in wind turbines, flag them as desired, or carry out predictive maintenance on wind turbines.
Power Line Detection Dataset
The Power Line Detection Dataset contains 16,078 images, consisting of two classes of images (Powerline and No_Powerline). It was originally curated to build robust object detection models for aerial objects such as drones, helicopters, and airplanes to detect power lines in motion. Many accidents occur when drones or other aerial objects crash into powerlines. These types of accidents destroy the powerlines and could lead to the loss of lives and property.
The computer vision model built from this data would be able to detect whether there is a powerline in view. This dataset can also be used to build an anomaly detection computer vision model to monitor power lines and detect when a power line falls to the ground or breaks. The energy dataset is divided into 10,000 train images (5417 images containing powerlines and 4583 images not containing powerlines) and 6,078 test images that can be used to evaluate the computer vision model’s accuracy.
Power Transmission Line Dataset
The Power Transmission Line Dataset is a collection of 1,044 images of power transmission lines. 348 images were acquired in a real-world scenario, while the remaining 696 were generated in a virtual, synthetic environment. These energy datasets can be used to build computer vision models for real-time power transmission line inspection. The images are segmented into three distinct classes, each representing their different geometric properties.
The real images are labeled “circuito_real’ (real circuit), while the synthetic images are either labeled as ‘circuito_simples’ (simple circuit) or circuito_duplo’ (double circuit). 348 images represent these classes, 232 for training, and 116 for testing/validation. The real images are typically disposed of in a messier arrangement. A central line distinguishes the simple circuit samples between the four extreme wires, while the double circuit shows four extreme lines without a central line.
Wind Turbine Detection Dataset
The Wind Turbine Detection Dataset contains 1,742 overhead images of wind turbines with their corresponding YOLOv3 formatted labels. This image dataset can be used for wind turbine object detection using aerial objects like drones. The image labels contain the x and y rectangular coordinates and the height and width of the bounding boxes for each wind turbine in the corresponding image.
These images were originally obtained from Power Plant Satellite Imagery before being hand-labeled and converted into formatted labels. Additional preprocessing steps were performed on the images to compress their dimensions to 608x608. This image dataset can be used to build computer vision models that can be used to perform aerial inspections of wind turbines. The images contained in this dataset have resolutions ranging from 0.6m to 1m.
Object Detection Dataset - Wind Turbines
This Wind Turbines dataset contains 2,885 images of wind turbines in dynamic and changing backgrounds. It is ideal for use by drone photographers and drones in general. Computer vision engineers can use this dataset to build computer vision models that can easily be interfaced with drone SDKs for detecting drones amongst a myriad of energy infrastructures, such as power transmission lines and cable towers.
The dataset was formatted using YOLO v7 PyTorch and has been ore-split into 2,643 train images, 130 test images, and 247 validation images (87:9:4). Object detection computer vision models are quite useful when performing automated infrastructure inspection and remote data collection on energy infrastructure facilities.
Challenges and Opportunities Facing Computer Vision in the Energy Infrastructure Industry.
Imagine a world where robots with eagle eyes protect our power lines, wind turbines, and other energy infrastructure. With computer vision aids, these robots can detect little fissures and flaws before they become major disasters. While this technology offers a safer, more efficient, and environmentally friendly energy infrastructure, some challenges remain.
- Data Scarcity: The energy industry suffers from a major deficiency in open CV energy datasets. There aren’t enough image datasets on energy infrastructure that can be used to build computer vision models in the industry. All computer vision applications in the energy infrastructure industry will remain possibilities until the right data is found and applied. Data scarcity is the major blockage hindering the use of computer vision in the energy industry.
- Algorithmic Limitations: The energy infrastructure is complicated, and many things happen simultaneously. Computer vision algorithms are trained to predict only one thing at a time and cannot detect multiple things in real time. Also, computer vision algorithms must learn from training data, which is not always available. These and other limitations of the algorithms make it difficult to use computer vision in the energy industry.
- Environmental Complexities: A computer vision model trained to detect energy infrastructure anomalies using images captured on a bright day may not work well when it rains or snows. This is because the model was not trained on data collected in different weather conditions, which can lead to low accuracy when making real-time predictions in varying weather conditions. Environmental complexities such as weather conditions, lighting, and other factors can affect the accuracy of computer vision algorithms. Therefore, it is important to carefully evaluate the accuracy of computer vision models under varying conditions before implementing them.
- Financial Limitations: The use of computer vision in the energy infrastructure industry is heavily dependent on funding. Training large-scale computer vision models, testing them, and embedding them on small-scale IoT devices costs a lot of money. Although this technology can help companies save money in the long run, it requires significant short-term investment.
Computer vision technology has the potential to offer significant advantages to the energy infrastructure industry. Although there are some limitations, there are ways to overcome them.
Firstly, machine learning engineers should ensure that they use ethical energy datasets that are fair and inclusive to train their computer vision models. Their energy datasets should account for all possible weather variations. They should not be biased towards a particular weather condition unless the computer vision model is designed to function only during that weather condition.
Secondly, computer vision engineers should use data augmentation techniques when working with biased data to reduce the effect of the bias. Data augmentation is a technique used by machine learning engineers to increase the size of a small dataset by making minor changes to the dataset or using deep learning to generate new points. These changes could include rotation, shear, scaling, and so on. Data augmentation helps reduce bias in datasets by accounting for different variations that could exist in production.
Finally, the government and other data awareness organizations should perform mass sensitization to educate energy firms on the importance of storing their infrastructural data. While this does not seem like a lot, it could go a long way in changing the orientation of industry leaders toward data storage and management.
Conclusion
Energy datasets are essential for advancing energy infrastructure. They can improve inspection, maintenance, and decision-making. Using these datasets, energy businesses can enhance efficiency, safety, and reduce downtime. The potential for computer vision in the energy infrastructure sector is vast, with applications such as monitoring wind turbine health and detecting pipeline breaches. Further investment in research, open-source data sharing, and collaboration is crucial for advancing computer vision in the industry. Doing this would go a long way toward expediting the vast adoption of computer vision in the energy infrastructure industry.
