Unmanned Aerial Vehicle (UAV) image target detection holds significant value for a wide range of applications in modern society. However, due to the variable flight altitude of UAV, the captured images often exhibit significant differences at the target scale and contain a large number of small targets. The existing methods are difficult to adapt to these changes, resulting in a decrease in detection accuracy. To address this issue, this paper proposes a new method for UAV image object detection based on full scale feature aggregation and grouped feature reconstruction FFAGRNet. Firstly, existing feature fusion methods are hindered by the layer-by-layer transfer structure, which limits effective information exchange between feature maps of different scales. In response, we propose the Full-scale Feature Aggregation module (FFA), which performs scale adaptation and information aggregation across multiple sets of feature maps, producing high-quality aggregated feature maps. Secondly, to further refine aggregation features and eliminate redundancy, we introduce the Grouping Feature Reconstruction module (GFR). This module subdivides aggregation features into multiple sub-level features, allowing them to autonomously learn channel and spatial layouts of target features. Lastly, we present the Parallel Super-resolution Semantic Enhancement module (PSSE) to reconstruct deep feature maps and incorporate spatial contextual information, effectively increasing the proportion of semantic information and enhancing the model’s ability to classify ambiguous targets. To validate the effectiveness of our proposed method, extensive experiments were conducted on the VisDrone2021 and UAVDT datasets. The results demonstrate that compared to the baseline, our method achieves a significant improvement in mAP50, with increases of 7.6% and 4.6% respectively, showcasing excellent performance compared to existing methods.

Recently, video saliency prediction has attracted increasing attention, yet the improvement of its accuracy is still subject to the insufficient use of multi-scale spatiotemporal features. To address this issue, we propose a 3D convolutional Multi-scale Spatiotemporal Feature Fusion Network (MSFFNet) to achieve the full utilization of spatiotemporal features. Specifically, we propose a Bi-directional Temporal-Spatial Feature Pyramid (BiTSFP), the first application of bi-directional fusion architectures in this field, which adds the flow of shallow location information on the basis of the previous flow of deep semantic information. Then, different from simple addition and concatenation, we design an Attention-Guided Fusion (AGF) mechanism that can adaptively learn the fusion weights of adjacent features to integrate them appropriately. Moreover, a Framewise Attention (FA) module is introduced to selectively emphasize the useful frames, augmenting the multi-scale temporal features to be fused. Our model is simple but effective, and it can run in real-time. Experimental results on the DHF1K, Hollywood-2, and UCF-sports datasets demonstrate that the proposed MSFF-Net outperforms existing state-of-the-art methods in accuracy.

Object detection on UAV images is a recent research hotspot. Existing object detection methods have achieved good results on general scenes, but there are inherent challenges with UAV images. The detection accuracy of UAV images is limited by complex backgrounds, significant scale differences, and densely arranged small objects. To solve these problems, we propose a UAV image object detection network based on Self-attention Guidance and Multi-scale Feature fusion (SGMFNet). Firstly, we design a Global-Local Feature Guidance module (GLFG). This module can effectively combine local information and global information, which makes the model focus on the object area and reduces the impact of complex background. Secondly, an improved Parallel Sampling Feature Fusion module (PSFF) is designed to efficiently fuse multi-scale features. Thirdly, we design an Inverse-residual Feature Enhancement module (IFE), which is embedded in the front of the newly added detection head to enhance feature extraction on small objects. Finally, we conduct a large number of experiments on the VisDrone2019 dataset. The results show that the proposed SGMFNet outperforms other popular methods, and has achieved good results in many scenarios.

With the rapid development of the unmanned aerial vehicle (UAV) industry, UAV image object detection technology has become a hotspot. However, due to a large number of dense small objects in UAV images, quickly and effectively detecting objects and achieving accurate classification is still a challenge. With this observation, we propose an efficient object detection network for UAV images based on cross-layer feature aggregation (CFANet). Firstly, we design a novel cross-feature aggregation module (CFA) to aggregate features at different scales on the basis of avoiding semantic gaps, so as to replace common features for feature fusion and achieve accurate detection. This method makes up for the defect that the layer-by-layer feature transfer method only focuses on the features of the previous layer and cannot fully integrate spatial and semantic information. Secondly, a layered associative spatial pyramid pooling module (LASPP) is proposed to capture context information while maintaining the sensitivity of feature maps at different layers to detail information. Thirdly, the alpha-IoU loss function is introduced to accelerate the convergence speed of the model and improve the detection accuracy. Finally, an adaptive overlapping slice (AOS) for high-resolution images is proposed to protect the integrity of the object when slicing. To verify the effectiveness of the proposed method, extensive experiments on challenge datasets for object detection in UAV images VisDrone2021 and UAVDT datasets are carried out. The results show that, compared with the other most advanced detectors, the proposed method can achieve significant performance on the basis of ensuring real-time detection.