LGI-DETR：用于无人机目标检测的局部-全局交互

无人机（UAV）已在 various fields中得到广泛应用。然而，大多数用于无人机的 existing object detectors not end-to-end and require the design of various complex components and careful fine-tuning。大多数 existing end-to-end object detectors are designed for natural scenes，direct application to UAV images并不理想。为解决上述挑战，我们设计了一个 local-global information interaction DETR for UAVs，称为 LGI-DETR。Cross-layer bidirectional low-level and high-level feature information enhancement，this fusion method is effective especially in the field of small objection detection。At the initial stage of encoder，我们提出了 local spatial enhancement module（LSE），which enhances the low-level rich local spatial information into the high-level feature，and reduces the loss of local information in the transmission process of high-level information。At the final stage of the encoder，我们提出了 novel global information injection module（GII），designed to integrate rich high-level global semantic representations with low-level feature maps。This hierarchical fusion mechanism effectively addresses the inherent limitations of local receptive fields by propagating contextual information across the feature hierarchy。在 two challenging UAV image object detection benchmarks（VisDrone2019 and UAVDT）上的 experimental results show that our proposed model outperforms the SOTA model。Compared to the baseline model，AP and AP50 improved by 1.9% and 2.4%，respectively。