General deep learning methods achieve high-level semantic feature representation by aggregating hierarchical features, which performs well in object detection tasks. However, issues arise with general deep learning methods in UAV-based remote sensing image object detection tasks. Firstly, general feature aggregation methods such as stride convolution may lead to information loss in input samples. Secondly, common FPN methods introduce conflicting information by directly fusing feature maps from different levels. These shortcomings limit the model’s detection performance on small and weak targets in remote sensing images. In response to these concerns, we propose an unmanned aerial vehicle (UAV) object detection algorithm, IF-YOLO. Specifically, our algorithm leverages the Information-Preserving Feature Aggregation (IPFA) module to construct semantic feature representations while preserving the intrinsic features of small objects. Furthermore, to filter out irrelevant information introduced by direct fusion, we introduce the Conflict Information Suppression Feature Fusion Module (CSFM) to improve the feature fusion approach. Additionally, the Fine-Grained Aggregation Feature Pyramid Network (FGAFPN) facilitates interaction between feature maps at different levels, reducing the generation of conflicting information during multi-scale feature fusion. The experimental results on the VisDrone2019 dataset demonstrate that in contrast to the standard YOLOv8-s, our enhanced algorithm achieves a mean average precision (mAP) of 47.3%, with precision and recall rates enhanced by 6.3% and 5.6%, respectively.

In this paper, a high-isolation two-port co-linearly polarized (co-LP) composite antenna is proposed. Dipole and W8JK array modes with co-LP omnidirectional patterns can be simultaneously supported by a plate with two etched back-to-back tapered slots. A microstrip differential and a T-divider coupling feeding networks are developed to selectively excite the dipole and W8JK modes, which results in a high isolation and low correlation between two co-LP ports without using any decoupling structures. Reflecting ground is applied to realize the directional radiation. Measurement results show that the overlapped operating band of the antenna can cover 3.4 - 4.1 GHz with low measured envelope correlation coefficients (< 2.5 × 10 ^-3 ) and high isolation (> 20 dB). Dual-polarized four-port element with good performances can be easily obtained based on the proposed co-LP antenna. The simulated results of the four-port prototype indicate that the proposed antenna is a promising candidate for the future compact massive MIMO array antenna.