Supervisor:
Southwest Ordnance Industry Bureau
Organizer:
Chongqing Ordnance Industry Society
Chongqing University of Technology
Chongqing University of Technology
Remote sensing image detection method based on global context attention
| DOI: | 10.11809/bqzbgcxb2024.02.036 |
| Keywords: | YOLOv5; remote sensing image; Varifocal Loss; global context attention; dynamic convolution |
| Abstract: | A target detection algorithm that integrates global contextual attention is proposed to address the issues of poor detection accuracy and missed detections caused by complex remote sensing image scenes, varying target sizes, and excessively small target sizes. This algorithm proposes a module that integrates global context attention mechanism and C3 structure to enhance the network’s ability to capture global features of images; The Varifocal Loss function is used to improve the detection performance of dense small targets; A normalized attention module is adopted to reduce less significant features and weights in the image, enabling the network to achieve higher detection accuracy; By using dynamic convolution to learn information from various dimensions, the trained model can reduce GFLOPs while maintaining improved detection accuracy. The experimental results on the NWPU VHR 10 dataset showed mAP of 96.0%, accuracy of 98.2%, and recall of 94.9%, which were 1.8%, 4.7%, and 2.2% higher than the original YOLOv5 model, respectively, demonstrating the effectiveness of the improved YOLOv5 method. |
| Published: | 2024-02-28 |