| Abstract |
Objective Remote sensing object detection technology has been widely used in various fields,including remote sensing mapping,smart cities,rural revitalization,resource exploration,national security,and military affairs. Particu⁃ larly with the completion and improvement of notable Chinese projects in high-resolution Earth observation systems,strong data support and development opportunities are available for remote sensing object detection. Traditional detection algo⁃ rithms have relatively weak generalization capabilities and are easily affected by noise,data distribution,and other factors. Consequently,challenges are encountered in overcoming the diverse scales and directions of remote sensing objects,as well as complex data distribution. In recent years,the strong representation learning capability and generalization capacity of deep learning have led to its widespread use in the field of object detection. Current detection algorithms based on deep learning can be simply divided into three categories:region-,pixel-,and query-based methods. Region-based methods typically offer high detection accuracy but require high computation and exhibit low efficiency. Pixel-based methods are usually single-stage detectors with low computation and high efficiency,but they often struggle with lower detection accu⁃ racy,particularly in small object detection tasks. Query-based methods require a large amount of data and have low effi⁃ ciency. However,the object features extracted using the aforementioned methods become overshadowed by the background information as the network deepens due to the complexity of background information and the many small objects in remote sensing images. This condition is not conducive to subsequent detection tasks,leading to limited final detection perfor⁃ mance. In response to the issue of insufficient small object detection performance under complex backgrounds,this work proposes a local parameter-free attention YOLO (you only look once) network (LPFA-YOLO) based on YOLOv5s. Method First,a local parameter-free attention(LPFA)mechanism is proposed. This mechanism can improve the attention for objects within a local region based on current features,without introducing any trainable parameters,thus constructing a bottleneck with parameter-free attention(BPFA). The attention mechanism in this block only assigns corresponding weights to the residuals and does not have a notable impact on the main weights. This approach helps avoid the problem of gradient disappearance and accelerates convergence by utilizing pre-trained weights. The C3 with attention module(C3A) constructed based on BPFA is then embedded into different stages of the backbone network to cater to objects of different scales. The shallow stage enhances the features of small objects,whereas the deep stage improves the features of medium and large objects,thereby realizing multiscale object feature enhancement and background information suppression,which addresses the issue of redundant background information. On this basis,the Wasserstein distance is utilized to measure the similarity of bounding boxes. A combined measurement method called Wasserstein-complete intersection of union (W-CIoU)and the related loss function are developed. This method can alleviate the sensitivity of small objects to position deviation and enable the separation of objects of different scales. Consequently,this method alleviates the issue of label misallocation due to the substantial difference between the anchor box and ground truth,reducing the missed detection rate of small objects. Result Experiments are conducted on two datasets and compared with seven advanced algorithms,includ⁃ ing EfficientNet,YOLOv4,detection Transformer(DETR),Swin Transformer,detecting objects with recursive feature pyramid and switchable atrous convolution(DetecoRS),dynamic anchor boxes for DETR(DAB-DETR),and YOLOv8s. On the remote sensing object detection(RSOD)dataset,the mean average precision(mAP)reaches 98. 2%,which is 0. 9% and 0. 3% higher than the baseline and YOLOv8s,respectively. The average precision for small objects(APS) reaches 42. 7%,showing a 2. 4% improvement compared to the baseline. The average precision for medium(APM)and large(APL)objects reach 67. 7% and 82. 1%,respectively,both demonstrating improvements compared to the baseline. On the remote sensing super-resolution object detection(RSSOD)dataset,the mAP achieves 87. 4%,which is 2. 7% and 2. 6% higher than the baseline and YOLOv8s,respectively. The APs increases to 38. 0%,revealing a 2. 7% rise compared to the baseline. The APM and APL achieve 52. 7% and 46. 4%,respectively,both showing improvements compared to the baseline. Simultaneously,the model has the fewest number of parameters and computation compared to contrast algo⁃ rithms. Experiments are conducted using two external remote sensing images obtained from Google Earth to assess the gen⁃ eralization of the algorithm. The results indicate that the algorithm detects all objects of interest,demonstrating good gener⁃ alization capabilities. Conclusion In this work,a YOLO network model based on local parameter-free attention is intro⁃ duced. The experimental results demonstrate that this model can effectively address the requirements of small object detec⁃ tion in complex scenes compared to several existing methods. © 2025 Editorial and Publishing Board of JIG. All rights reserved. |
