UAV VEHICLE DETECTION BASED ON IMPROVED YOLO-X MODEL AND SUPER-RESOLUTION ENHANCEMENT

We proposed a vehicle detection model based on improved YOLO-X on VisDrone2019 dataset for industrial inspections and traffic monitoring. This paper proposes an offline and online staged data augmentation method for local feature samples for the first time. The FocalLoss was further improved and replaced by the cross-entropy loss that was previously common in the industry. The target detection Head combination was redesigned, and the model detection accuracy mAP was 0.042 higher than the industry general model. The algorithm based on extended sensing domain dynamically adjusted the recognition range in real time, and the runtime frame rate was increased by 19%, which provided the feasibility for the implementation of ARM deployment of real-time vehicle detection.