BSFCDet: Bidirectional Spatial–Semantic Fusion Network Coupled with Channel Attention for Object Detection in Satellite Images

Abstract

Due to the increasing maturity of deep learning and remote sensing technology, the performance of object detection in satellite images has significantly improved and plays an important role in military reconnaissance, urban planning, and agricultural monitoring. However, satellite images have challenges such as small objects, multiscale objects, and complex backgrounds. To solve these problems, a lightweight object detection model named BSFCDet is proposed. First, fast spatial pyramid pooling (SPPF-G) is designed for feature fusion to enrich the spatial information of small targets. Second, a three-layer bidirectional feature pyramid network (BiFPN-G) is suggested to integrate the deep feature’s semantic information with the shallow feature’s spatial information, thus improving the scale adaptability of the model. Third, a novel efficient channel attention (ECAM) is proposed to reduce background interference. Last, a new residual block (Resblock_M) is constructed to balance accuracy and speed. BSFCDet achieves high detection performance while satisfying real-time performance, according to experimental results.