YDGAN-TL: Enhanced YOLOv8 with DCGAN and Transfer Learning for UAV Small Object Detection in Remote Sensing

The application of deep learning for small object detection in Unmanned Aerial Vehicle (UAV) remote sensing imagery has garnered increasing attention across various domains. However, current detection methods for small objects face significant challenges, including background noise and poor data quality, which adversely affect their performance. This paper proposes an enhanced YOLOv8 model through Deep Convolutional Adversarial Network (DCGAN)-based data augmentation and transfer learning strategies to address these limitations. The data augmentation strategy employs DCGAN to emphasize the representation of small objects while mitigating the impact of background noise and data quality issues. Subsequently, transfer learning techniques with various fine-tuning strategies are applied to improve the performance of the existing YOLOv8 model in the context of UAV small object Detection. Experimental results on VisdroneDET-2019, CARPK, and HIT-UAV datasets demonstrate that the proposed YDGAN-TL model achieves superior performance, as indicated by metrics such as mean Average Precision (mAP) for small objects and a reduction in model parameters. Notably, the effectiveness of the YDGAN-TL model is further corroborated by enhanced performance metrics, including precision and recall, with mean values reaching 0.95 and 0.97, respectively. These findings suggest that the enhanced model offers promising results across multiple performance parameters, indicating its potential as a robust solution for UAV small object detection.