CADDN: A Content-Aware Downsampling-Based Detection Method for Small Objects in Remote Sensing Images

Linping Zhang, Yu Liu, Xueqian Wang, You He, Gang Li, Yiming Zhang, Chang Liu, Zhizhuo Jiang, Yang Liu

Abstract: A key issue of existing deep-learning-based object detection methods in remote sensing images is that they often struggle to differentiate the background and small object regions due to multilevel downsampling operations therein. Downsampling operations help extract high-level semantic features but result in excessive loss of spatial features of small objects. In this article, we propose a new small object detector using multispectral remote sensing images, named content-aware downsampling-based detection network (CADDN), where we newly design a content-aware downsampling-based module (CADM). Unlike conventional downsampling operations that apply uniform downsampling parameters across the entire feature map, CADM adaptively assigns higher weights to feature elements that are critical for distinguishing objects from the background, and this assignment is guided by the contextual awareness of object locations during the downsampling process. Experiments based on multispectral remote sensing images with small ships and vehicles demonstrate that CADM can accurately identify and preserve the locations of important object-related features, and CADDN correspondingly achieves superior small object detection performance than state-of-the-art methods.

External IDs:dblp:journals/tgrs/ZhangLWHLZLJL25