Research on Target Detection Model of Remote Sensing Image Based on Dual Attention Yolo

With the widespread application of remote sensing technology, accurate and rapid detection and identification of targets in remote sensing images have become an important research area. Traditional target detection methods often face issues with low recognition accuracy and slow response when dealing with complex backgrounds or small targets. This study proposes an improved YOLO (You Only Look Once) model integrated with a dual attention mechanism to enhance the performance of target detection in remote sensing images. This mechanism utilizes spatial attention to increase the model's sensitivity to target locations, while channel attention enhances the recognition of target features, enabling more accurate identification of small and complex environment targets. The model was tested across multiple remote sensing image datasets, and the results show significant improvements in detection accuracy and real-time performance compared to traditional YOLO models. Additionally, the implementation of the model reveals the potential of the dual attention mechanism in improving the efficiency and accuracy of remote sensing image processing. With further optimization and adjustment, this model is expected to play a significant role in various application scenarios such as environmental monitoring, urban planning, and disaster assessment.