SDaDCS Remote Sensing Target Detection Algorithm

In the field of remote sensing, the rapid and accurate acquisition of the category and location of airplanes has emerged as a prominent research. However, remote sensing fuzzy imaging and complex environmental interference affect airplane detection. Besides, the inconsistency in the size of remote sensing images and the low accuracy of small target detection are crucial challenges that need to be addressed. To tackle these issues, we propose a novel network SDaDCS (SAHI-data augmentation-dilation-channel and spatial attention) based on YOLOX model and the slicing aided hyper inference (SAHI) framework, a new data augmentation technique and dilation-channel and spatial (DCS) attention mechanism. Initially, we create a remote sensing dataset for airplane targets and introduce a new data augmentation technique based on the Rotate-Mixup and mixed data augmentation to enhance data diversity. The DCS attention mechanism, which comprises the dilated convolution block, channel attention and spatial attention, is designed to bolster the feature extraction and discrimination of the network. To address the challenges arised by the difficulties of detecting small targets, we integrate the YOLOX model with the SAHI framework. Experiment results show that, when compared to the original YOLOX model, the proposed SDaDCS remote sensing target detection algorithm enhances overall accuracy by 13.6%. The experimental results validate the effectiveness of the proposed algorithm.