UAV - Aided Road Damage Identification with YOLOv9

This paper presents an imaginative approach to mechanized street harm location utilizing the YOLOv9 calculation and profound learning strategies. Conventional strategies of recognizing street harm are difficult and perilous, but UAVs and AI innovations offer upgraded proficiency and exactness. Leveraging YOLOv9, our strategy centers on recognizing and localizing different sorts of street harm inside UAV images, pointing to expand independent street upkeep frameworks by instantly recognizing and informing support companies of harms. This venture presents novel classifications of asphalt harm and proposes strategies custom-made for UAVcaptured scenarios, tending to challenges such as shifting elevations, points, and lighting conditions. These upgrades guarantee higher precision and unwavering quality in harm discovery. Our framework decreases the require for manual assessments, relieving dangers to human auditors, and clears the way for more astute, more proficient street support arrangements, in this manner laying the foundation for future headways and inquire about in robotized road harm discovery.