Deep Learning-Based Instance Segmentation of Aircraft in Aerial Images using Detectron2

The project aims to leverage the advanced features of the Detectron2 framework for object detection and instance segmentation in computer vision. The goal is to achieve state-of-the-art accuracy and robustness in detecting and segmenting objects in images, especially in scenarios with small or complex shapes and cluttered backgrounds. To accomplish this, the project uses the latest deep learning models based on the Faster R-CNN and Mask R-CNN architectures, which employ the region proposal network (RPN) and feature pyramid network (FPN) modules, respectively. The models are trained on large-scale datasets such as COCO and fine-tuned on specific domains to improve their performance on target objects. The models are also augmented with additional techniques such as data augmentation, multi-scale training, and hyperparameter optimization. The project further enhances the models by incorporating state-of-the-art techniques such as Cascade R-CNN, which uses a series of R-CNN models with increasing IoU thresholds to improve object detection accuracy. The project also explores using weighted feature fusion and self-attention mechanisms to capture object context better and improve segmentation accuracy. To evaluate the effectiveness of the proposed approach, the project conducts extensive experiments on various datasets, including COCO, YOLO, and custom datasets. The experimental results show that the proposed approach using Detectron2 and deep learning models achieves state-of-the-art performance in object detection and instance segmentation tasks, outperforming other state-of-the-art methods. Overall, the project demonstrates the effectiveness of using the latest deep learning models and advanced techniques to improve object detection and instance segmentation in computer vision. The proposed approach provides a valuable contribution to the field of computer vision and paves the way for future research in this area.