DSAA-YOLO: UAV remote sensing small target recognition algorithm for YOLOV7 based on dense residual super-resolution and anchor frame adaptive regression strategy

Abstract

The challenges posed by high pixel resolution and complex backgrounds in UAV remote sensing images have hindered the effective feature extraction and precise bounding box regression for small targets. In response to these challenges, numerous detection methods based on deep learning have emerged in recent years. Despite their advancements, these methods have not fully addressed the demand for accurate identification of small targets in remote sensing images. This paper introduces DSAA-YOLO, a novel algorithm designed for small target detection in UAV remote sensing images. Firstly, a new data augmentation strategy, termed Super Resolution Data Augment (SRDA), is proposed, which integrates the concept of image super-resolution to enrich the dataset while preserving data quality. Furthermore, a Dense Residual-based Super-Resolution module (DRSR) is introduced to enhance the resolution of small targets that have undergone quality degradation due to transformations. Subsequently, an Information Alignment Feature Enhancement Module (IAFE) is proposed to maximize the extraction of original features from the image. Finally, based on the improved Multi-Objective Grey Wolf Optimization (MOGWO), a novel dynamic anchor regression strategy termed Multi-Object Golf Dynamic Anchor (MGDA) is devised to generate more precise bounding boxes. The proposed DSAA-YOLO algorithm demonstrates significant improvements over current state-of-the-art methods in terms of widely recognized metrics including mAP, and AP50 on the VisDrone dataset.

Keywords

• Anchor box regression
• Small target detection
• UAV remote sensing images
• Super-resolution
• Multi-Objective Grey Wolf Optimization