A Lightweight Aerial Image Object Detector Based on Mask Information Enhancement

Object detection in unmanned aerial vehicle (UAV) imagery is highly valuable in both military and civilian domains. However, deploying conventional detectors is challenging due to limited computing resources on UAV platforms. In addition, UAV images often contain densely arranged objects, demanding higher accuracy from detectors. In response to these challenges, we proposed the lightweight aerial object detection method based on mask feature enhancement, named BFDet. First, to enhance the feature extraction capability of the model under limited parameters, we designed the balanced channel attention layer (BCA Layer). The BCA Layer improves the extraction of high-quality channel information during feature processing. Second, to enhance model performance while minimizing parameters, we designed a feature extraction network utilizing the efficient feature extraction module (EFEM) and the downsampling module (DM) as its backbone. This design reduces the number of parameters while improving the extraction of discriminative features. Third, to enrich the semantic information of features, we proposed a progressive space pyramid pooling (PSPP) module, obtaining features with rich semantic information to enhance model performance. Finally, to tackle the challenge of detecting densely packed objects, we proposed a mask information enhancement module (MIEM) to obtain the fine-grained features, enhancing the model’s discriminative ability in dense scenes. The proposed BFDet underwent extensive experimentation on both the VisDrone and UAVDT datasets. The mAP0.5 and FPS values of BFDet reach 51.4% and 33, respectively, on the VisDrone dataset, and the parameter number is only 5.6 M.