A real-time vehicle detection method in unmanned aerial vehicle images with selective contextual features

Detecting small and occluded objects in unmanned aerial vehicle (UAV) images remains a critical challenge. The inferior feature quality of these small and occluded objects leads to incomplete feature extraction, resulting in missed detections. To address this challenge, we propose an innovative detector based on ObjectBox to enhance detection performance and reduce missed detections of small and occluded objects by incorporating the neck called selective fused deformable context feature path aggregation network (SFDCFPAN) and the decoupled head. Firstly, we designed a neck called the selective feature path aggregation network (SFPAN) to fuse features and reduce the loss of spatial information. Subsequently, we provide a feature extraction module named fused deformable context feature extraction module (FDC) to model object shapes and then fuse context features to obtain the object’s semantic and spatial information. We employ the FDC module as the feature extraction module at specific locations within four feature layers of SFPAN, denoted as SFDCFPAN, to enhance the detector’s feature extraction and modeling capabilities. Lastly, we introduce a decoupled head structure to alleviate the mutual interference between classification and localization tasks. We conduct a comparative analysis of our detector with popular detectors on the VisDrone2019 and the UAVDT sub-dataset. Experimental results demonstrate the superior performance of our detector, achieving high accuracy on the two datasets while meeting real-time constraints. Furthermore, we integrate SFPAN and SFDCFPAN into various detectors. Experimental results exhibit the substantial enhancement in detector accuracy achieved by these feature fusion frameworks without compromising real-time performance, demonstrating the applicability to existing detectors.