Balanced Density Regression Network for Remote Sensing Object Counting

Counting objects in remote sensing is crucial for analyzing their distribution in images. Compared to surveillance perspectives, counting dense objects in remote sensing images is more challenging due to the smaller sizes of these targets. Recently, many methods utilize Gaussian convolution regression to estimate the count of dense objects in remote sensing images. However, most methods ignore the issue of regression imbalance inherent in Gaussian distribution, which is caused by the numerical differences in the center and edge regions. To tackle this challenge, we propose a Balanced Density Regression Network (BDRNet) to mitigate regression inaccuracies in Gaussian distributions due to numerical variances. Different from other methods, we divide the regression problem into two steps: first focusing on the regions of interest, then achieving precise regression. BDRNet consists of an Adaptive Kernel Weighting Attention (AKWA) mechanism and a Pixel-wise Occupancy Prediction (PwOE) module. Firstly, AKWA is designed to acquire accurate semantic feature information, which is obtained by learning the weights of dilated convolutions with different sizes of receptive fields. Secondly, the PwOE module applies Gaussian position embeddings to point labels to constrain the network to focus on the object region without increasing annotation cost. Finally, the integration of pixel-wise occupancy prediction features and kernel weighting features forms multi-layer cross-attention mechanisms, facilitating channel-level feature interaction and improving density regression predictions. Thus, the center and edge regions of the Gaussian kernel are treated equally, and the regression is balanced. Additionally, Extensive experiments on diverse datasets validate the effectiveness of the method, resulting in preferable performance. The code is available at: https://github.com/HotChieh/BDRNet.