AN ATTENTION-INTEGRATED YOLO11-S MODEL FOR OBJECT DETECTION IN SATELLITE IMAGERY: PERFORMANCE ANALYSIS AND COMPARISON
TL;DR
A novel model is introduced by assessing the impacts of several YOLO object detection algorithms with the Convolutional Block Attention Module (CBAM) on aircraft detection from satellite images to demonstrate that attention mechanisms have a significant impact when used with the YOLO architecture for object detection in satellite images.
Abstract
As remote sensing technologies improve, we are now able to look at the Earth from different points of view. These technologies have enabled major changes in many areas. High-resolution satellite images have enabled progress in civilian and military applications such as environmental monitoring, disaster management, and public safety. While these images give us much information, we also need advanced analysis and automated object detection. Recent studies employing deep learning architectures reached high success rates in object detection from satellite imagery. This study introduces a novel model by assessing the impacts of several YOLO object detection algorithms with the Convolutional Block Attention Module (CBAM) on aircraft detection from satellite images. We used the HRPlanesv2 dataset for our experiments. The results revealed that the proposed model had better performance compared to alternative models. The proposed model achieved a mAP50 of 0.9868, a mAP50-95 of 0.7911, a precision of 0.9811, and a recall of 0.9637. Incorporating CBAM improves the detection of objects in crowded and complex scenes. These results demonstrate that attention mechanisms have a significant impact when used with the YOLO architecture for object detection in satellite images. It also provides a reliable and efficient solution for practical applications requiring accurate and consistent aircraft detection.