CAW-YOLO: Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing.

In recent years, there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks. Despite these efforts, the detection of small objects in remote sensing remains a formidable challenge. The deep network structure will bring about the loss of object features, resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers. Additionally, the features of small objects are susceptible to interference from background features contained within the image, leading to a decline in detection accuracy. Moreover, the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty. In this paper, we introduce a novel approach, Cross-Layer Fusion and Weighted Receptive Field-based YOLO (CAW-YOLO), specifically designed for small object detection in remote sensing. To address feature loss in deep layers, we have devised a cross-layer attention fusion module. Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention (BRA). To enhance the model's capacity to perceive multi-scale objects, particularly small-scale objects, we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule. Furthermore, wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance (NWD) and Efficient Intersection over Union (EIoU) losses. The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets. The experimental results unequivocally demonstrate the model's pronounced advantages in small object detection for remote sensing, surpassing the performance of current mainstream models. [ABSTRACT FROM AUTHOR]