Transfer Learning for Object Detection in Remote Sensing Images with YOLO

Abstract

This investigation explores exchange learning methodologies for protest locations in inaccessible detecting pictures utilizing the YOLO design. Four unmistakable exchange learning calculations, specifically YOLO Fine-Tuning, Highlight Extraction, Space Versatile Preparing, and Knowledge Distillation are investigated and assessed on an assorted dataset. The tests illustrate noteworthy advancements in location execution, with YOLO Fine-Tuning accomplishing an exactness of 0.85, review of 0.78, F1 score of 0.81, and mean average precision (mAP) of 0.75. Highlight Extraction grandstands competitive comes about, with an accuracy of 0.87, a review of 0.80, an F1 score of 0.83, and a mAP of 0.78. Domain Adaptive Training exhibits predominant execution, accomplishing an exactness of 0.89, review of 0.82, F1 score of 0.85, and mAP of 0.80. Information Refining yields promising results, with a precision of 0.88, review of 0.81, F1 score of 0.84, and mAP of 0.79. These discoveries highlight the viability of exchange learning algorithms in upgrading the adaptability and precision of YOLO for protest locations in diverse inaccessible detecting scenarios. The study contributes important bits of knowledge to the field of further detecting, emphasizing the viable appropriateness of tailored exchange learning techniques for real-world applications.