AFE-YOLOv8: A Novel Object Detection Model for Unmanned Aerial Vehicle Scenes with Adaptive Feature Enhancement

Abstract

Object detection in unmanned aerial vehicle (UAV) scenes is a challenging task due to the varying scales and complexities of targets. To address this, we propose a novel object detection model, AFE-YOLOv8, which integrates three innovative modules: the Multi-scale Nonlinear Fusion Module (MNFM), the Adaptive Feature Enhancement Module (AFEM), and the Receptive Field Expansion Module (RFEM). The MNFM introduces nonlinear mapping by exploiting the property that deformable convolution can dynamically adjust the shape of the convolution kernel according to the shape of the target, and it effectively enhances the feature extraction capability of the backbone network by integrating multi-scale feature maps from different mapping branches. Meanwhile, the AFEM introduces an adaptive fusion factor, and through the fusion factor, it adaptively integrates the small-target features contained in the feature maps of different detection branches into the small-target detection branch, thus enhancing the expression of the small-target features contained in the feature maps of the small-target detection branch. Furthermore, the RFEM expands the receptive field of the feature maps of the large- and medium-scale target detection branches through stacked convolution, so as to make the model’s receptive field cover the whole target, and thereby learn more rich and comprehensive features of the target. The experimental results demonstrate the superior performance of the proposed model compared to the baseline in detecting objects of various scales. On the VisDrone dataset, the proposed model achieves a 4.5% enhancement in mean average precision (mAP) and a 5.45% improvement in average precision at an IOU threshold of 0.5 (AP50). Additionally, ablation experiments conducted on the challenging DOTA dataset showcase the model’s robustness and generalization capabilities.