The operational environment of UAVs poses unique challenges for object detection compared to conventional methods. When UAVs capture remote sensing images from elevated altitudes, objects often appear minuscule and can be easily obscured by complex backgrounds. This increases the likelihood of false positives and missed detections, thereby complicating the detection process. Furthermore, the hardware resources available on UAV platforms are typically highly constrained. To meet deployment requirements, researchers often must compromise some detection accuracy in favor of a more lightweight model. To address these challenges, we propose PS-YOLO, a fast and precise network specifically designed for UAV-based object detection. In the proposed network, we first design a lightweight backbone based on partial convolution. Then, we introduce a more efficient neck network called FasterBIFFPN to replace the original PAFPN, enabling more effective multi-scale feature fusion. Finally, we propose the GSCD head. GSCD employs shared convolutions to enhance the network’s ability to learn common features across objects of different scales and introduces Normalized Gaussian Wasserstein Distance Loss (NWDLoss) to improve detection accuracy. This detection head effectively increases inference speed without significantly increasing parameter counts. The proposed PS-YOLO is validated on the Visdrone2019 dataset, and the results demonstrate that PS-YOLO provides a 2% improvement in precision, 0.5% improvement in recall, 1.3% improvement in mean average precision (mAP), 41.3% reduction in parameter counts, 6.1% reduction in computational cost, and 26.73 FPS improvement in inference speed compared to the benchmark model YOLOv11-s.

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.

Segmenting small infrared targets presents a significant challenge for traditional image processing architectures due to the inherent lack of texture, minimal shape information, and their sparse pixel representation within images. The conventional UNet architecture, while proficient in general segmentation tasks, inadequately addresses the nuances of small infrared target segmentation due to its reliance on downsampling operations, such as pooling, which often results in the loss of critical target information. This paper introduces the Channel Spatial Attention Nested UNet (CSAN-UNet), an innovative architecture designed specifically to enhance the detection and segmentation of small infrared targets. Central to CSAN-UNet’s design is the Cascaded Channel and Spatial Convolutional Attention Module (CSCAM), a novel component that adaptively enhances multi-level features and mitigates the loss of target information attributable to downsampling processes. Additionally, the Channel-priority and Spatial Attention Cascade Module (CPSAM) represents another pivotal advancement within CSAN-UNet, prioritizing channel-level adjustments alongside spatial attention mechanisms to efficiently extract deep semantic information pertinent to small infrared targets. Empirical validation conducted on two public datasets confirms that CSAN-UNet surpasses existing state-of-the-art algorithms in segmentation performance, while simultaneously reducing computational overhead.