Research on Optical Remote Sensing Image Target Detection Technology Based on AMH-YOLOv8 Algorithm

Abstract

Aiming at the YOLO (You Only Look Once) algorithm’s low detection accuracy caused by the complex background environment and large target scale difference in optical remote sensing image detection, the lightweight convolution fusion attention mechanism based AMH-YOLOv8 (Attention Mechanisms Hybrid- YOLOv8) target detection algorithm is proposed in this paper. In this algorithm, the BiFormer attention mechanism is added to enhance the detection performance for small targets. Effectively captures local and global information in remote sensing images, improving the accuracy and generalisation of target detection; secondly, using the lightweight convolution GSConv instead of the original normal convolution. Effectively reduces model size while ensuring that model performance is not compromised. And optimised computation and parameter count improvements due to BiFormer; finally, the SPPCSPC space pyramid structure was added. Effectively enhances the feature extraction capability of the model and reduces the probability of missed and false detections.In order to verify the effectiveness of the algorithm, experimental analyses were conducted on two publicly available datasets, namely DIOR and DOTA. Experimental results indicate that the AMH-YOLOv8 algorithm achieved an impressive detection accuracy of 87.6% and 72.9% in [email protected], The results show that the algorithm in this paper has improved the effectiveness of target detection in optical remote sensing images. And it can better cope with the problems of complex background environment, dense distribution of small targets and large differences in target scales.

Keywords

• Optical remote sensing image
• target detection
• YOLO
• biformer attention mechanism
• lightweight convolution GSConv
• SPPCSPC space pyramid structure