The ever-changing global conditions are creating a dent in the environment. So, there’s a need for mitigation strategies and approaches to make the entire arena cleaner and greener. In recent times, littering issues have been one of the major concerns that hold a considerable risk of developing a catastrophe. The collaboration of artificial intelligence and image processing helps humanity in many ways to keep the environment clean. It is like the alert generation system that monitors the environment in real-time and discloses any mishap at the earliest.
Oceans and seas are the primary hubs of littering plastics and wastes. According to the IUCN, every year, over 300 million tonnes of plastic trash are produced. Out of this, around 8 million tonnes end up in the oceans and seas. So, these plastic wastes form about 80% of the marine debris. Plastic pollution is a threat to food safety and quality, human health, and coastal tourism. They also enhance global climatic change that is not good for the environment. Hence, it forms a vicious cycle that will increase global warming and harm aquatic life. Therefore, it becomes essential to understand the current conditions and move forward accordingly.
AI builds cognitive solutions associated with human minds, such as thinking, learning, and problem-solving. AI makes the camera smarter. If one captures a picture, AI may help to recognize the face or any other body part. It understands the environment by the image processing and accordingly sets the tone and adjusts the settings to give a better quality of the image. AI cameras develop the dataset by automatically identifying and recognizing scenes and the magnitude of the light available. Then it compares the data with the trained dataset to adjust the parameters and ensure good images get more space.
AI Camera & Litter Detection
At present, for litter detection or removing the plastic wastes from the water bodies, there are labor-intensive methods available. So, the humans conduct the field-level counts, keeping the cleaning process away from mechanization and takes more time to accomplish the task. Also, it is almost impractical to detect and know all the locations where marine debris is present. To bring more automation in the process, scientists have developed a new method that uses artificial intelligence in image processing to detect the wastes accumulating in the oceans and the seas. The technology is using two branches of AI – computer vision and deep learning. Computer vision is like tutoring the computers to understand and recognize images, videos, and other data, whereas deep learning involves processing the data and delivering consolidated analysis.
How was the technology developed?
Extracting the best practices of both the branches of AI, scientists collaborated with Microsoft’s Azure cloud computing services. It helped to create an automated litter detection system. The dataset obtained uses two applications of computer vision – object detection and image classification. In object detection, during the image processing, the camera gives a label to the object by locating its position. Image classification uses object detection to provide more than one tag to, unlike items. The trained dataset then compares any object captured. For litter detection, it can classify different objects like food packaging, beverage bottles, and cups. Researchers also built a robust machine learning model to detect synthetic (computer-generated) images to ensure that no fake pictures are available in the dataset. Once the overall product developed, scientists and researchers tested it in Thames, Buriganga, and Hobart’s stormwater channels.
An AI-based approach is more responsive than a manual human network. It provides capacity-building measures to enhance waste management in the oceans and seas. The method can get more advancement if drones, along with waste removal systems, add this model. The addition of drones will ensure broader coverage and detect the debris in a lesser time. In a nutshell, an AI camera litter detection system has enormous potential to create a better aquatic and marine ecosystem.