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AnAI-PoweredSystemforReal-TimeUnderwaterPlastic Detection and Classification Using YOLOv7 and VGG16
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This study proposes a hybrid AI architecture combining YOLOv7 for real-time detection and VGG16 for detailed classification of underwater plastic waste, including bottles, bags, nets, and wrappers, with SMTP notification for automated alerts. The system demonstrates high accuracy and performance under challenging underwater conditions including turbidity and low visibility, offering a practical tool for marine waste monitoring and rapid response.
Plastic pollution in marine environments poses a severe threat to aquatic ecosystems and global biodiversity. The traditionalmethodsusedtomonitorthewastesintheseaaretimeconsumingandinvirtuallyeverysituation,notrealtimeresponsive.Inorder to achieve effective and efficient plastic underwater detection and classification, the current paper proposes a hybrid architecture thatincorporatesYOLOv7,anewly emergingobjectdetectorarchitecture,and VGG16,adeep convolutionalneuralnetwork. The system is able to detect the plastic waste and classify it into different categories, such as bottles, bags, nets, and wrappers. An SMTP notification system is proposed to simplify the detection, while an email notification with annotated detection result is recommended to besentateach plastic detection. Itcould beto train YOLOv7 on large amounts ofunderwater images to predict wheretheyare ingeneralandthenuseVGG16to predictexactly wheretheyareincroppedregions.Thetestsshowthatthesystem ishighlyaccurateandperformantunderextremeconditionsintheunder-seaenvironment,includingturbidityorlowvisibility.The automatic alerter also provides early alertingand immediate response and remediation. Overall, the bookis a brilliant, measured, terrestrialefforttosolvetheproblemofmarinewastedisposalandcontributestothebodyofliteratureonenvironmentalprotection and building a sustainable ecosystem.
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