Advances in synthetic aperture radar (SAR) technology, combined with innovations in deep learning algorithms, have established SAR as a critical method for ship detection. However, in some complex environments, such as harbors and inshore areas, false and missed detections tend to occur because of intricate background interference and the minute proportions of ship targets. To overcome these issues, this paper presents a ship detection model, CSS-YOLO, for SAR images in complex scenes. First, a vertically compressed backbone network (VCCB) is proposed, which reduces feature loss and enhances the extraction of detailed features by decreasing the number of downsampling and increasing the feature extraction width. Second, a wavelet convolution-based preprocessing downsampling module (WPCM) is proposed to obtain a larger receptive field and reduce noise to obtain finer features. Third, a short-range aggregated decentralized necking network (SADNet) is proposed to reduce the loss of detail, thereby improving the efficiency of feature fusion. To validate the effectiveness of the method, this paper conducts experiments on three datasets, HRSID, SSDD, and LS-SSDD-v1.0, for performance evaluation. Compared with other widely used methods, CSS-YOLO demonstrates excellent performance with mAP0.5 values of 94.8%, 98.6%, and 78.4% on HRSID, SSDD, and LS-SSDD-v1.0 datasets, respectively. Evaluation results demonstrate that CSS-YOLO outperforms state-of-the-art SAR ship detection approaches in accuracy and overall performance.

Due to the multiscale characteristics of ship targets in ORSIs (optical remote sensing images), ship target detection in ORSIs based on depth learning is still facing great challenges. Aiming at the low accuracy of multiscale ship target detection in ORSIs, this paper proposes a ship target detection algorithm based on multiscale feature enhancement based on YOLO v4. Firstly, an improved mixed convolution is introduced into the IRes (inverted residual block) to form an MIRes (mixed inverted residual block). The MIRes are used to replace the Res (residual block) in the deep CSP module of the backbone network to enhance the multiscale feature extraction capability of the backbone network. Secondly, for different scale feature maps’ perception fields, feature information, and the scale of the detected objects, the multiscale feature enhancement modules—SFEM (small scale feature enhancement module) and MFEM (middle scale feature enhancement module)—are proposed to enhance the feature information of the middle- and low-level feature maps, respectively, and then the enhanced feature maps are sent to the detection head for detection. Finally, experiments were implemented on the LEVIR-ship dataset and the NWPU VHR-10 dataset. The accuracy of the proposed algorithm in ship target detection reached 79.55% and 90.70%, respectively, which is improved by 3.25% and 3.56% compared with YOLO v4.

In image processing and computer vision, image segmentation plays a fundamental role since it can make images easier to analyze. However, noise is easily introduced into images and brings great challenges to image segmentation. This paper focuses on the segmentation problem of noisy images and proposes an efficient variational level set model based on adaptive local fitted image to handle it. By utilizing normalized local entropy and local means, an adaptive local fitted image is proposed and introduced into the data term to enhance the robustness of the model against noise. Then a penalty term is proposed to reduce the deviation of the adaptive local fitted image from the original image by punishing the difference between them, so as to guarantee the accuracy of segmentation results. Later, the total variational regularization term is introduced into the model, so the level set function can be smoothed and the effect of noise on the active contour can be further reduced. The energy functional of the whole model is convex rigorously, which can reach the minimum and should have good properties in noisy image segmentation. Numerous experiments on synthetic, natural, synthetic aperture radar and oil spill images demonstrate that the proposed model is strongly robust to different types and levels of noise, which indicates its good performance in noisy image segmentation.

Unmanned aerial vehicle (UAV) image object detection has become an increasingly important research area in computer vision. However, the variable target shapes and complex environments make it difficult for the model to fully exploit its features. In order to solve this problem, we propose a UAV image object detection method based on a backbone feature reuse detection network, named BFRDNet. First, we design a backbone feature reuse pyramid network (BFRPN), which takes the model characteristics as the starting point and more fully utilizes the multi-scale features of backbone network to improve the model’s performance in complex environments. Second, we propose a feature extraction module based on multiple kernels convolution (MKConv), to deeply mine features under different receptive fields, helping the model accurately recognize targets of different sizes and shapes. Finally, we design a detection head preprocessing module (PDetect) to enhance the feature representation fed to the detection head and effectively suppress the interference of background information. In this study, we validate the performance of BFRDNet primarily on the VisDrone dataset. The experimental results demonstrate that BFRDNet achieves a significant improvement in detection performance, with the mAP increasing by 7.5%. To additionally evaluate the model’s generalization capacity, we extend the experiments to the UAVDT and COCO datasets.

Infrared images usually lack significant texture and morphological features, while there is a large amount of noise and clutter in the background. These characteristics make it difficult for general-purpose target detection algorithms to adequately extract key features of small targets in infrared images, resulting in incomplete feature information. In addition, the direct feature fusion of traditional neck networks suffers from feature misalignment, and the features of small targets are easily diluted or masked, further exacerbating the problem of feature loss. To solve the above-mentioned problems, this article proposes a dual-stream cooperative sensing detection network (DCSDNet) specifically designed for infrared small target detection. First, to improve the feature extraction capability of the model, this article proposes the dense grouped feature learning module, which provides a richer feature representation base for the infrared small target detection task through multilevel feature processing. Meanwhile, to reduce the problems of information loss and fusion imbalance, the refined feature fusion bottleneck module is designed to achieve a finer feature fusion, which improves the feature extraction accuracy and fusion effect. In addition, this article critically re-examines the necessity of feature pyramids through experimental validation and heatmap visualization. We deliberately discard the conventional pyramid structure prevalent in multiobject detection systems, and propose a feature harmonization backbone. This innovation not only reduces feature dilution and misalignment risks but also strengthens the model’s feature extraction capacity. Ultimately, combining these improvements forms the DCSDNet target detection algorithm, which focuses on improving the detection of small infrared targets. The DCSDNet method proposed in this article achieves a mAP0.5 of 68.1%, 95.4%, and 83.4% on the VEDAI-Infrared, NUAA-SIRST, and IRSTD-1 K datasets, respectively. These data indicate that the algorithm achieves deep feature extraction of small targets in infrared images and shows superior performance in IR small target detection.

High-resolution remote sensing image semantic change detection (SCD) helps to accurately capture the spatial distribution and dynamic evolution of carbon sources and sinks by identifying changes in land cover types. However, existing methods suffer from the loss of spatial details and insufficient ability to model global features. Therefore, this letter proposes an SCD model based on spatiotemporal attention perception and multiscale fusion (SC-SCDNet). The model introduces a multiscale efficient cross-attention (MCA) block in the encoder to bridge the semantic gap, and integrates a feature enhancement module (FEM) to enhance the semantic expression ability of small targets using multibranch dilated convolution. In addition, a spatiotemporal channel window interaction module (TBCM) is designed to capture global information from both spatial and channel dimensions, enhancing spatial detail expression. The experimental results show that SC-SCDNet achieves the most advanced performance on SECOND and Landsat-SCD datasets, providing a better technical scheme for carbon sources and carbon sinks change detection.