DET-YOLO: An Innovative High-Performance Model for Detecting Military Aircraft in Remote Sensing Images

Abstract

To address the challenges of low detection rate and high missed detection rate of military aircraft in current complex remote sensing data, and to meet the requirements of real-time detection and easy deployment of models, this article introduces DET-you only look once (YOLO), an innovative detection model. First, to tackle the issue of reduced accuracy in identifying small targets amidst intricate backgrounds, a novel feature extraction component, C2f_DEF, was devised. This module replaced all existing C2f components within YOLOv8n, thereby significantly enhancing the model's ability to cope with complicated environmental contexts. Second, to achieve the functionality of easy deployment of the model, some deep structures were simplified to make the model more lightweight. Afterward, to further improve the model's ability to handle complex backgrounds and dense environments in remote sensing images and to improve the model's detection accuracy for military aircraft, the DAT module was embedded in the model. Finally, this article also optimized the loss function and reg_max to further reduce computational costs while improving the detection accuracy of the model. To verify the effectiveness and strong universality of DET-YOLO, extensive experimental verification was conducted on three publicly available datasets, namely MAR20, NWPU VHR-10, and NEU-DET. On the MAR20 dataset, compared with other advanced models, DET-YOLO achieved the highest mAP0.5 (namely 94.7%) with only 80 training epochs while meeting lightweight and real-time requirements. While on the other two datasets, DET-YOLO also achieved the best detection performance.

Keywords

• Attention mechanism
• deep learning
• deformable convolution (DCN)
• loss function
• remote sensing images (RSIs)
• you only look once (YOLO)