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

作　　者：Weiya Shi Shaowen Zhang Shiqiang Zhang 

机构地区：[1]College of Artificial Intelligence and Big Data,Henan University of Technology,Zhengzhou,450001,China [2]College of Information Science and Engineering,Henan University of Technology,Zhengzhou,450001,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期3209-3231,共23页工程与科学中的计算机建模（英文）

基　　金：supported in part by the National Natural Science Foundation of China under Grant 62006071;part by the Science and Technology Research Project of Henan Province under Grant 232103810086.

摘　　要：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.

关 键 词：Small object detection attention mechanism cross-layer fusion discrete cosine transform 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]