A Decoupled Semantic–Detail Learning Network for Remote Sensing Object Detection in Complex Backgrounds

Abstract

Detecting multi-scale objects in complex backgrounds is a crucial challenge in remote sensing. The main challenge is that the localization and identification of objects in complex backgrounds can be inaccurate. To address this issue, a decoupled semantic–detail learning network (DSDL-Net) was proposed. Our proposed approach comprises two components. Firstly, we introduce a multi-receptive field feature fusion and detail mining (MRF-DM) module, which learns higher semantic-level representations by fusing multi-scale receptive fields. Subsequently, it uses multi-scale pooling to preserve detail texture information at different scales. Secondly, we present an adaptive cross-level semantic–detail fusion (CSDF) network that leverages a feature pyramid with fusion between detailed features extracted from the backbone network and high-level semantic features obtained from the topmost layer of the pyramid. The fusion is accomplished through two rounds of parallel global–local contextual feature extraction, with shared learning for global context information between the two rounds. Furthermore, to effectively enhance fine-grained texture features conducive to object localization and features conducive to object semantic recognition, we adopt and improve two enhancement modules with attention mechanisms, making them simpler and more lightweight. Our experimental results demonstrate that our approach outperforms 12 benchmark models on three publicly available remote sensing datasets (DIOR, HRRSD, and RSOD) regarding average precision (AP) at small, medium, and large scales. On the DIOR dataset, our model achieved a 2.19% improvement in mAP@0.5 compared to the baseline model, with a parameter reduction of 14.07%.