Object detection model with efficient feature extraction and asymptotic feature fusion for unmanned aerial vehicle image

Object detection in drone unmanned aerial vehicle (UAV) imagery is increasing. However, existing lightweight object detection models still face challenges in UAV aerial image object detection tasks due to the variable object scale and the existence of dense small objects. We propose a lightweight object detection model named YoloV8-RFCA. For small object features, the channel attention mechanism is integrated with receptive-field convolution to construct the Receptive-field conv with Channel Attention (RFCA) attention module, removing the parameter sharing issue and enhancing the feature extraction capability of the backbone network. Focusing on the feature information loss and degradation caused by multi-level transmission during the feature fusion operation, an asymptotic feature fusion strategy is proposed. Related experiment results indicate that the model achieved 82.5 mAP on the PASCAL VOC dataset and 41.9 mAP on the VisDrone2019 dataset. These experimental results confirm that our proposed model has a high practical application value in the field of UAV aerial image object detection.