Full-Scale Feature Aggregation and Grouping Feature Reconstruction-Based UAV Image Target Detection

Abstract

Unmanned Aerial Vehicle (UAV) image target detection holds significant value for a wide range of applications in modern society. However, due to the variable flight altitude of UAV, the captured images often exhibit significant differences at the target scale and contain a large number of small targets. The existing methods are difficult to adapt to these changes, resulting in a decrease in detection accuracy. To address this issue, this paper proposes a new method for UAV image object detection based on full scale feature aggregation and grouped feature reconstruction FFAGRNet. Firstly, existing feature fusion methods are hindered by the layer-by-layer transfer structure, which limits effective information exchange between feature maps of different scales. In response, we propose the Full-scale Feature Aggregation module (FFA), which performs scale adaptation and information aggregation across multiple sets of feature maps, producing high-quality aggregated feature maps. Secondly, to further refine aggregation features and eliminate redundancy, we introduce the Grouping Feature Reconstruction module (GFR). This module subdivides aggregation features into multiple sub-level features, allowing them to autonomously learn channel and spatial layouts of target features. Lastly, we present the Parallel Super-resolution Semantic Enhancement module (PSSE) to reconstruct deep feature maps and incorporate spatial contextual information, effectively increasing the proportion of semantic information and enhancing the model’s ability to classify ambiguous targets. To validate the effectiveness of our proposed method, extensive experiments were conducted on the VisDrone2021 and UAVDT datasets. The results demonstrate that compared to the baseline, our method achieves a significant improvement in mAP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sub> , with increases of 7.6% and 4.6% respectively, showcasing excellent performance compared to existing methods.