Visual AI Takes Flight


Vol. 7 No. 3 // 2023

Modified Image – Birds eye view of the frame of a house being built on a construction site via Stock Market

How drones are revolutionizing surveillance and monitoring

Drones have revolutionized the way surveillance and monitoring are conducted by leveraging the power of visual artificial intelligence (AI). From security and defense to environmental monitoring and disaster response, visual AI transforms how we gather and analyze data. With the help of sophisticated cameras, sensors, and computer algorithms, drones can capture real-time footage, process it and generate meaningful insights for a wide range of applications.

The benefits of visual AI in surveillance and monitoring

Surveillance and monitoring can significantly benefit from the advanced technology of visual AI, which has various advantages. One of the significant advantages of visual AI is its ability to detect suspicious activities or events in real-time, alerting operators to potential threats before they escalate. It is possible because visual AI can analyze vast amounts of data and recognize patterns that may be difficult for human operators to detect, resulting in increased accuracy in surveillance and monitoring.

Another benefit of visual AI is its cost-effectiveness compared to traditional surveillance methods. Visual AI can provide a cost-effective alternative to traditional surveillance methods as it requires fewer personnel to operate and can cover a larger area. With enhanced situational awareness, operators have a complete picture of what is happening in a given area, enabling them to make informed decisions quickly. It can also monitor hazardous environments or situations that would be unsafe for human operators, improving safety in surveillance and monitoring operations.

Applications of visual AI with drones in surveillance

Real-world examples of the benefits of visual AI in surveillance and monitoring can be found in industries such as retail, transportation, and public safety in Austin, Texas . For instance, some retailers have used visual AI to track customer movements and analyze buying patterns to optimize store layouts and improve customer experiences. In transportation, visual AI monitors traffic patterns, detects accidents or congestion, and improves overall traffic safety.

We can see one example of visual AI in public safety in Austin – Texas, where SkyGrid, a Boeing, SparkCognition company, worked with Austin–Travis County EMS to demonstrate how search and rescue missions can be executed more efficiently with drones. The company showcased how SkyGrid’s Aerial OS™ provides rapid search and rescue capabilities using AI-based drone technologies. Using a unique patent-pending algorithm specialized for SAR missions, SkyGrid optimizes the distribution of drones over a defined space, calculating the most efficient routes for one or multiple vehicles within a given time frame. Drone operators can either specify the area they wish to survey and the size of their fleet to determine the time necessary to survey an area or define the area and time required for a survey to determine the optimal search pattern and the number of drones needed to complete a mission.

Anti-poaching efforts are one of the primary applications of drones in wildlife protection. With the help of drones, conservationists and law enforcement agencies can monitor vast areas and detect poaching activity in real time. Drones can also help locate poacher camps and provide evidence for legal action. In addition, drones equipped with speakers can play sounds that deter animals from entering poaching hotspots or alert park rangers of potential threats.

Another example is the transportation industry, where the Massachusetts Department of Transportation uses visual AI to monitor traffic patterns and reduce congestion. The technology is being used to analyze traffic flow and identify bottlenecks, allowing the department to take action to improve traffic flow and reduce accidents.

Applications of visual AI with drones in environmental monitoring

Drones have become essential for various industries, including wildfire detection, air quality monitoring, precision agriculture, water resource management, and pollution control. Drones equipped with visual AI can help monitor vast areas and detect anomalies that might not be visible to the human eye, enabling early detection and swift action.

In wildfire detection, drones with thermal cameras can quickly detect fires and provide valuable data to firefighting teams. For example, the California Department of Forestry and Fire Protection (Cal Fire) has used drones equipped with thermal cameras since 2018 to detect wildfires in remote areas. The drones can fly over the area, detect hot spots, and alert firefighters of potential fires in real time.

Air quality monitoring is another field where drones equipped with visual AI can provide valuable data. For instance, researchers at the University of Surrey in the UK have been using drones to measure air pollution levels in urban areas. The drones can fly over the area, measure the air quality in real time, and create a map of the pollution levels.

Precision agriculture is another area where drones equipped with visual AI can help increase efficiency and reduce costs. Drones can monitor crop health, identify areas of the field that need attention, and provide valuable data for irrigation and fertilization. For example, they use drones equipped with multispectral cameras in Japan to monitor rice crops and optimize irrigation and fertilization.

Drones with visual AI can also help with water resource management and pollution control. For instance, researchers at the University of California, Berkeley, have been using drones to monitor water quality in rivers and lakes. The drones can collect water samples, take pictures of the area, and analyze the data using visual AI to identify pollutants and contaminants.

Challenges and limitations of visual AI with drones in surveillance and monitoring

Visual AI with drones has its challenges and limitations. Using drones for surveillance has raised regulatory challenges, ethical concerns surrounding privacy and data security, and technical limitations that need to be considered.

Regulatory challenges include airspace regulations, data protection laws, and privacy laws. Airspace regulations vary by country and region, and drones used for surveillance must adhere to these regulations. For example, in the United States, the Federal Aviation Administration (FAA) regulates the operation of drones in national airspace. Data protection laws, such as the GDPR in the European Union, impose strict requirements on the collection, processing, and storage of personal data collected by drones. Privacy laws, such as the Fourth Amendment of the US Constitution, protect individuals from unreasonable searches and seizures and can be violated by using drones for surveillance.

Ethical concerns regarding the use of drones for surveillance include privacy, data security, bias, and discrimination. Using drones for surveillance can infringe on individuals’ privacy, leading to concerns about mass surveillance and potential abuse. Data collected by drones can also be vulnerable to hacking and data breaches, leading to data security concerns. Finally, the algorithms used to analyze drone data could be biased against certain individuals or groups, leading to issues of bias and discrimination. Visual AI with drones has brought a revolution in surveillance and monitoring, but it also has its technical limitations that need to be considered. Camera quality is one of the major limitations of visual AI with drones. The accuracy of the visual AI algorithms depends on the camera quality used to capture images and videos for analysis. Drones must have high-quality cameras to capture clear images and videos in different lighting conditions, including low-light environments.

Another limitation of visual AI with drones is processing power. Visual AI algorithms require significant processing power to analyze vast amounts of data in real time. However, drones have limited processing power compared to other computing devices, which can be challenging. As a result, some visual AI algorithms may not be suitable for use on drones. Storage limitations can be a significant challenge for drones used in surveillance and monitoring. Drones have limited storage capacity, and visual AI algorithms require large amounts of storage space to store the data they analyze. It can limit the amount of data that drones can collect continuously over a long period.

Environmental factors such as wind, rain, and fog can also affect the accuracy of the visual AI algorithms used in drones for surveillance and monitoring. These factors can interfere with the drone’s ability to capture clear images and videos, reducing the accuracy of the analysis. Therefore, using visual AI with drones requires careful consideration of the technical limitations to ensure their effectiveness in surveillance and monitoring.

Future of visual AI with drones in surveillance and monitoring

Visual AI and computer vision technology have already made significant advancements in surveillance and monitoring, but there is still much potential for further progress. Quantum computing is a relatively new technology that uses quantum-mechanical phenomena to perform calculations. It can revolutionize computer vision and visual AI by enabling faster and more efficient data analysis. Quantum computing could be beneficial in analyzing large datasets and in developing more advanced machine learning algorithms.

Swarm intelligence is a collective behavior that emerges from the interactions of numerous individual agents. It has already been successfully applied in drone technology and could improve visual AI and computer vision algorithms. For example, a swarm of drones could work together to analyze a large area in real time and provide a complete picture of a given situation.

Deep learning algorithms are a subset of machine learning that enable computers to learn from and decide based on large amounts of data. They are already being used in visual AI and computer vision applications, but there is potential for further advancement. For example, researchers are currently exploring the use of deep learning algorithms to enable computers to recognize and interpret human emotions from facial expressions.

Modified Image – Aerial view of construction workers working on a new building in Sao Paulo via Creative Market

Visual AI with drones is transforming the way surveillance and monitoring are conducted. By leveraging the power of computer vision, drones equipped with sophisticated cameras, sensors, and algorithms can capture and analyze data in real time, providing insights that were previously impossible to obtain. While there are challenges and limitations associated with this technology, the future of visual AI with drones looks promising as the technology continues to advance.

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