Unmanned aerial vehicles (UAVs) are now widely used in many fields. Due to the randomness of UAV flight height and shooting angle, UAV images usually have the following characteristics: many small objects, large changes in object scale, and complex background. Therefore, object detection in UAV aerial images is a very challenging task. To address the challenges posed by these characteristics, this paper proposes a novel UAV image object detection method based on global feature aggregation and context feature extraction named the multi-scale feature information extraction and fusion network (MFEFNet). Specifically, first of all, to extract the feature information of objects more effectively from complex backgrounds, we propose an efficient spatial information extraction (SIEM) module, which combines residual connection to build long-distance feature dependencies and effectively extracts the most useful feature information by building contextual feature relations around objects. Secondly, to improve the feature fusion efficiency and reduce the burden brought by redundant feature fusion networks, we propose a global aggregation progressive feature fusion network (GAFN). This network adopts a three-level adaptive feature fusion method, which can adaptively fuse multi-scale features according to the importance of different feature layers and reduce unnecessary intermediate redundant features by utilizing the adaptive feature fusion module (AFFM). Furthermore, we use the MPDIoU loss function as the bounding-box regression loss function, which not only enhances model robustness to noise but also simplifies the calculation process and improves the final detection efficiency. Finally, the proposed MFEFNet was tested on VisDrone and UAVDT datasets, and the mAP0.5 value increased by 2.7% and 2.2%, respectively.

With the advancements in deep learning and Synthetic Aperture Radar (SAR) technology, an increasing number of individuals are utilizing deep learning techniques to detect ships in SAR images. However, the efficiency of SAR ship detection is affected by complex background interference and various ship sizes. Addressing these challenges, this paper proposes a balanced feature-enhanced attention model (BFEA). First, we introduce a novel Attention Feature Fusion Network (WEF-Net) tailored for SAR multi-scale ship detection. WEF-Net effectively balances the information across different backbone layers and harmonizes semantic information from various levels of the feature pyramid through aggregation and averaging. Next, we embed the receiving field extension module (MRFFM) in WEF-Net to learn the context information and generate the global characteristics of the receiving field balance. In addition, it can extract features from multiple scales to enhance the detection capability of the model for ships of different scales. At the same time, acknowledging the impact of surrounding complex background interference on the detector, we redesigned the ELAN module by combining convolution and attention, This enhancement enables the model to better attend to target position information during feature fusion, suppress the surrounding complex background interference, and highlight the ship's feature information. Finally, owing to the prevalence of small targets in SAR images, we employ an optimized loss function to bolster the model's performance in detecting small targets. This approach accelerates training convergence, reduces instances of missed detection on small targets, and enhances overall detection performance across multiple scales. Experimental results demonstrate that our model achieves detection accuracies of 98%, 93.1%, and 76.9% on the SSDD, HRSID, and LS-SSDD-v1.0 datasets, respectively, effectively discerning ship targets amidst complex backgrounds in SAR images.

Object detection in unmanned aerial vehicle (UAV) imagery is highly valuable in both military and civilian domains. However, deploying conventional detectors is challenging due to limited computing resources on UAV platforms. In addition, UAV images often contain densely arranged objects, demanding higher accuracy from detectors. In response to these challenges, we proposed the lightweight aerial object detection method based on mask feature enhancement, named BFDet. First, to enhance the feature extraction capability of the model under limited parameters, we designed the balanced channel attention layer (BCA Layer). The BCA Layer improves the extraction of high-quality channel information during feature processing. Second, to enhance model performance while minimizing parameters, we designed a feature extraction network utilizing the efficient feature extraction module (EFEM) and the downsampling module (DM) as its backbone. This design reduces the number of parameters while improving the extraction of discriminative features. Third, to enrich the semantic information of features, we proposed a progressive space pyramid pooling (PSPP) module, obtaining features with rich semantic information to enhance model performance. Finally, to tackle the challenge of detecting densely packed objects, we proposed a mask information enhancement module (MIEM) to obtain the fine-grained features, enhancing the model’s discriminative ability in dense scenes. The proposed BFDet underwent extensive experimentation on both the VisDrone and UAVDT datasets. The mAP0.5 and FPS values of BFDet reach 51.4% and 33, respectively, on the VisDrone dataset, and the parameter number is only 5.6 M.

Change detection network for process-oriented model design improves detection efficiency through more complete time modeling. However, the networks accumulated by convolutional operations are limited by the localisation of convolutional kernels, resulting in limited perception of spatio-temporal relationships. Therefore, in this paper, a process-oriented change detection network based on discrete wavelet transform is proposed by combining the frequency domain information in the convolutional network. Specifically, the network constructs a dual-time image into a multi-frame video stream through video modelling and extracts the change features of different scales, frequencies and directions in video and image features from the frequency domain perspective with the help of discrete wavelet transform, which enhances the perception of spatio-temporal relationships. Experimental results on LEVIR-CD, GVLM-CD and EGY-BCD datasets validate the effectiveness of the network.