NAS-Kernel: Learning Suitable Gaussian Kernel for Remote-Sensing Object Counting
Liangliang Zhao, Junyu Gao, Xuelong Li
Abstract
The purpose of object counting is to estimate the number of specific kinds of objects in a given image. In remote sensing imagery, challenges arise in object counting due to issues like scale variations and complex backgrounds. Existing density map-based object counting methods have achieved satisfactory performance in some general scenarios ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., crowd counting and vehicle counting) and have become the mainstream methods. These density map-based counting methods use a fixed Gaussian kernel in the density map generation stage, thus they are not well adapted to the challenges such as scale variations present in remote sensing scenes. In this letter, we propose to use the strategy of neural architecture search (NAS-Kernel) to select appropriate Gaussian kernels corresponding to objects of different scales in the Gaussian density map generation stage. NAS-Kernel is a plug-and-play algorithm that can be used in other density map-based counting methods. In addition, a contextual path aggregation feature fusion strategy is proposed to fuse multi-scale feature information. The ablation experiments verify that the proposed method can significantly improve the performance of baseline. Experimental results on the four sub-datasets of RSOC show that the proposed method achieves state-of-the-art performance. On the Building sub-dataset, the proposed method achieves 18% and 12% lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) than the existing methods.