Garbage Detection Solution for Smart Cities Using YOLOv8 Image Recognition

Urban environments become more challenging as cities expand because waste management becomes more complex. The research proposes a solution for garbage detection by integrating drone technologies and pattern recognition through YOLOv8 image processing. This methodology employs drones fitted with high-resolution cameras positioned over urban centers to capture their respective images in real time. Using the YOLOv8 processor, images are received and debris is detected and classified accurately and rapidly. In addition, drones are equipped with GPS and GSM modules for accurate transmission of waste data to the central system, which improves waste management optimization. This technology accurately sends the location of trash areas.There is also a technology of geo-fencing added so that drones do not cross the region where there is is no connectivityity with the drones. The novelty of this solution is the synergy between aerial photography, YOLOv8, and specialized in-classification systems, which makes it possible to obtain high-resolution repeatable results. Experiments have shown that this system is superior to conventional waste detection systems scoring an outstanding 97 to 98 percent in detection accuracy. These methods reduce overhead costs and improve waste management in smart cities, demonstrating the innovative adoption of such technology.