Unmanned aerial vehicles (UAVs) or drones are rapidly gaining popularity in the ﬁeld of remote sensing for capturing images with ultra-high spatial resolution while ﬂying at lower altitudes. The development of highly-eﬃcient miniaturized sensors and the use of geospatial image processing techniques have been helpful in drone-based surveillance. Drones-based surveillance can be helpful in a lot of use cases, especially in rescue operations, infiltration monitoring, etc. According to a report undertaken by the European Emergency Number Association, drones (equipped with visible light sensors) in a rescue operation were able to find a victim three minutes quicker than a conventional mission.
But, can surveillance be automated with AI-powered drones?
Yes! Drone surveillance can be implemented using edge processing in computer vision. Before we dig into this topic, let’s take a look at the challenges faced by manufacturers of autonomous drones.
Electro-Optical (EO) sensors have been used as payloads for Drones for years. However, even EO sensors are not enough to automate the operation and eliminate human supervision. Drones with EO sensors need real-time monitoring or offline manual analysis of previously captured data and stored by the Drone. To fully leverage the potential of EO sensors and enable autonomous or semi-autonomous operations, intelligent video analytics is required, along with processing hardware that enables online implementation of such analysis in Drones. Some of the most relevant implementations of EO sensors are small target detection. Drone manufacturing companies need to find solutions to the challenges like;
Edge computing has given birth to the concept of edge AI. In Edge processing aggregation, data manipulation, bandwidth reduction, and other logic directly are executed on an IoT sensor or device. In case of drone surveillance – processed on drones itself.
The purpose is to keep basic computation as close as possible to the physical system for making the device as “smart” as possible. By implementing Edge processing, not every piece of data is sent to the cloud. Edge processing completes IoT devices, deals with some activities themselves, and does not depend on a cloud server to establish a control algorithm that even survives an Internet connection failure.
Edge AI enables algorithms to run locally on hardware devices, and the algorithms work based on data generated on the device. The problem here is that since neural networks assist most AI solutions today, running such systems at the edge of AI will need a lot of computing power. The challenge here is to ensure the high accuracy output of algorithms with low power consumption. However, the use of hardware options like graphics processing units (GPUs),application-specific integrated circuits (ASICs), and system-on-a-chip (SoC) accelerators has facilitated edge AI for the autonomous operation of drones.
The incorporation of AI to the edge enables drone manufacturers to use drone sensors to gather and implement visual and environmental data. This sensor data allows an autonomous drone flight, making operation easier with little or no human intervention. The independent nature of drones offers innovative mobility offerings and is now used for several commercial purposes.
Surveillance methods like GPS tracking, camera observation, stake-outs, data mining, biometric management, etc. These surveillance methods need human intervention to a level and their reach is limited. In most cases, cameras are handled manually or fixed upon a tripod for surveillance. All these surveillance methods might not be suitable for areas where secret monitoring is needed. Some of these instances are to fight terrorism, prevent crime and social unrest, protect national security, and more. Aerial surveillance using a helicopter achieves the desired result, but it is also very costly. So, what’re the feasible solutions then?
Autonomous Drone surveillance systems provide the ideal solution to the problems and solve the issues faced by other surveillance methods. Drone surveillance systems offer a more straightforward, faster, and cheaper way to collect data and several other key advantages like accuracy in data. Autonomous Drones can enter challenging to-reach areas and confined spaces with minimal noise. These drones can be equipped with night-vision cameras and sensors to gather data and imagery that the human eye cannot detect.
Several companies like Amazon, EasyJet, Walmart, DHL are using Drones to simplify their day-to-day operations. Drones are equipped with various surveillance tools and equipment to collect high-definition video and still images. They can also be configured to intercept phone calls, find GPS locations, and gather license plate information.
Drones are used to survey areas and monitor animal populations, insects, plants, etc. Private parties often use drones for recreation, video recording, research, and journalism. When we talk about drones, you might be thinking that a drone operator must be needed to operate the drone, right? NO.
A self-flying drone is in-built with computerized programming, propulsion and navigation systems, GPS, sensors and cameras, programmable controllers, and much more to intelligent surveillance drones. AI moves into smart devices to automate the operations. Edge processing enables data collection and residing in the exact location. Edge data processing goes hand-in-hand with more privacy and more data transfer security with no hardware restrictions. This is the reason manufacturers locate more complicated AI algorithms at the edge to make Drones autonomous.
Military drones are well known for using AI and used for more than a decade. These autonomous drones are used for surveying or critical operations. Military personnel can easily replace live pilots with AI-based drones and extensively map areas with autonomous security drone surveillance methods.
AI-based drones can potentially reduce human costs by reducing the likelihood of an attack on human lives. It also increases stealth in secret operations as AI-based drones are significantly smaller than other surveillance aircraft and less likely to be noticed by the rival.
Drones can add vital functionality to humanitarian and disaster relief efforts by immediately assessing the damage. Which in turn cuts response time and allows more efficient deployment of relief resources. Drones also give ultra-location-specific readings, which can forecast fires, earthquakes, and other natural disasters.
The drone captures images, and the AI provides accurate real-time predictions of the level of damage like forest fires, tsunamis, floods, etc. It also does the job to mark the safe areas and the areas that need further investigation.
Drones are a massive help for search and rescue in unsafe conditions for humans to venture into. Drones are used to scan avalanche areas with infrared and identify trapped skiers or hikers. AI-based drones enable rescuers to survey a much larger area much faster, speeding response times and save lives.
Another use case of AI-based drones is identifying a vehicle used while kidnapping by flying over busy routes or high traffic in a city. A human might overlook or be unable to reach a car due to haste; AI-based drones can find the vehicle models and number plates without slowing down.
Most construction companies are using AI-based drones to monitor construction sites continuously and automatically. Drones enable faster evaluation and eliminate the need to walk through areas or climb structures that might be dangerous for humans.
AI-powered drone systems can track vehicles on crowded roads or places in less time with help of video analytics. These video analytics algorithms are trained in the various datasets to set the accuracy of the detection. For example, a stolen car can be detected from the aerial view of the drone using AI-powered video analytics. Various metrics like color, size, and model of the product can be identified using these algorithms
AI-powered video analytics have the potential to inspect the quantity of a particular object. Object counting features can be beneficial in events and programs where the footfall is high. The AI-powered analytics could render the number of objects, people, etc with the help of the autonomous drone, and the AI-powered algorithm can address the quality with a detailed report.
Inspection is an integral part of many industries, quality of inspection is of utmost importance but often it’s not able to match the standards due to human error or lack of potential for detailed inspection. AI-powered video analytics could solve this problem with the help of drone surveillance. Detailed analytics over each product or department of the industry could be attained with the help of this solution