Advance Attention-Based Techniques for Small Object Detection in Remote Sensing Images

Most studies in remote sensing have focused on identifying and recognizing small, distant objects within large scenes. Detecting small objects, a particular subset of object detection, encounters significant difficulties due to image resolution, object size, quantity, and orientation. Current methods frequently rely on deep neural networks to create strong feature representations, which results in substantial computational requirements. To tackle these issues, we implemented Advance Attention Mechanism (AAM) consisting of Combined Channel and Spatial Attention (CCSA) and Atrous Spatial Pyramid Pooling (ASSP) to boost detection accuracy. Our model outperforms baseline model and provides a detection accuracy of 82.00% in overall performance.