Background Suppression Network With Attention Collapse Inhibited Transformer for Optical Remote Sensing Object Detection

Object detection for remote sensing imagery (RSI) has been extensively exploited in practical applications. However, similar and multiscale objects in RSI, especially small objects, pose challenges to RSI object detection methods. Particularly, existing approaches ignore irrelevant background in RSI leading to hardship in discriminative feature extraction, resulting in instances of false positive (FP) and false negative (FN) of similar objects. In this article, we propose an irrelevant background suppression network (IBS-Net), which employs the structure of a convolutional neural network (CNN) in series with a Transformer to efficiently capture local and global information in images to respond the challenge of multiscale object detection. Primarily, a background detach module (BDM) is designed behind the backbone to suppress the irrelevant background and enhance the foreground to minimize the interference of irrelevant background for object detection. Furthermore, a composite-sampler (C-S) is devised to sample the vectors describing the foreground and the context, which expands the limited receptive field of the detector to better distinguish similar objects. Especially, considering that transformer-based object detection methods suffer from an attention collapse issue that leads to a degradation of the network representation. An attention collapse inhibited transformer (ACI-former) is presented by designing a partial residual connection, which induces the network to perceive more target information and reduces the loss of small target features thus improving the detection accuracy of small objects. Ultimately, we have conducted related experiments on two benchmarks, which demonstrate that our method has achieved prominent results compared with other mainstream detection methods.