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Object detection on satellite and aerial images has gained the attraction of the scientific community of computer vision due to its immense value, high difficulty, and the development of large-scale datasets enough to train deep learning models. The progress is tremendous with the increase in the precision of the fast single-stage object detection models which used to sacrifice precision for speed. Aerial and satellite images are of large sizes which makes slow models infeasible for production. Single-shot Alignment Network (S2A-Net) is a fast and competitive single-stage model in terms of precision. However, there is a potential to increase its precision in detecting small and cluttered objects in a complex background. In this paper, a new hybrid approach by incorporating Instance Level Denoising (ILD) module from Small, Cluttered, and Rotated Object Detector &#x002B;&#x002B; (SCRDet&#x002B;&#x002B;) into S2A-Net is proposed. The model was trained and tested on Dota V1.0. The proposed model achieves a higher mean average precision (mAP) than S2A-Net to be 79.73&#x0025; and when it was trained using the Kullback&#x2013;Leibler divergence as a regression loss function, the proposed model can reach as high as 80.39&#x0025; mAP.

Improving Small and Cluttered Object Detection by Incorporating Instance Level Denoising Into Single-Shot Alignment Network for Remote Sensing Imagery