Ship detection in SAR images is crucial in both civilian and military fields, offering extensive application prospects. Nonetheless, owing to the distinctive characteristics of SAR imaging, this task confronts numerous challenges. Specifically, ships with high aspect ratios, dense arrangements and small sizes in complex environments frequently yield in suboptimal positioning effects, consequently impacting detection performance. In response to the challenges in ship target detection, this paper introduces a novel approach, termed Inner-Alpha-CIOU (IA-CIOU), that relies on an enhanced Intersection over Union (IOU). Primarily, the method introduces Inner IOU, which effectively regulates generation of auxiliary bounding boxes through scale factor r. This ensures a better fit for dimensions of ship target frames, thereby enhancing target detection performance as well as expediting model convergence. Subsequently, this method introduces Alpha IOU, enhancing robustness of small-size ship targets in complex backgrounds by adjusting α. This allows the detector to achieve greater flexibility in ship regression accuracy. Following numerous experimental validations, proposed algorithm consistently outperforms on both SAR-Ship-Dataset, MSAR-1.0 dataset and SSDD dataset. This groundbreaking innovation not only possesses immeasurable practical worth, but also introduces a fresh perspective together with enlightening insights for future research efforts.

High-altitude UAV photography presents several challenges, including blurry images, low image resolution, and small targets, which can cause low detection performance of existing object detection algorithms. Therefore, this study proposes an improved small-object detection algorithm based on the YOLOv5s computer vision model. First, the original convolution in the network framework was replaced with the SPD-Convolution module to eliminate the impact of pooling operations on feature information and to enhance the model’s capability to extract features from low-resolution and small targets. Second, a coordinate attention mechanism was added after the convolution operation to improve model detection accuracy with small targets under image blurring. Third, the nearest-neighbor interpolation in the original network upsampling was replaced with transposed convolution to increase the receptive field range of the neck and reduce detail loss. Finally, the CIoU loss function was replaced with the Alpha-IoU loss function to solve the problem of the slow convergence of gradients during training on small target images. Using the images of Artemisia salina, taken in Hunshandake sandy land in China, as a dataset, the experimental results demonstrated that the proposed algorithm provides significantly improved results (average precision = 80.17%, accuracy = 73.45% and recall rate = 76.97%, i.e., improvements by 14.96%, 6.24%, and 7.21%, respectively, compared with the original model) and also outperforms other detection algorithms. The detection of small objects and blurry images has been significantly improved.

Pine wilt disease (PWD) is one of the most destructive forest diseases that has led to rapid wilting and mortality in susceptible host pine trees. Spatially explicit detection of pine wood nematode (PWN)-induced infestation is important for forest management, policy making, and practices. Previous studies have mapped forest disturbances in response to various forest diseases and/or insects over large areas using remote-sensing techniques, but these efforts were often constrained by the limited availability of ground truth information needed for the calibration and validation of moderate-resolution satellite algorithms in the process of linking plot-scale measurements to satellite data. In this study, we proposed a two-level up-sampling strategy by integrating unmanned aerial vehicle (UAV) surveys and high-resolution Radarsat-2 satellite imagery for expanding the number of training samples at the 30-m resampled Sentinel-1 resolution. Random forest algorithms were separately used in the prediction of the Radarsat-2 and Sentinel-1 infestation map induced by PWN. After data acquisition in Muping District during August and September 2021, we first verified the ability of a deep-learning-based object detection algorithm (i.e., YOLOv5 model) in the detection of infested trees from coregistered UAV-based RGB images (Average Precision (AP) of larger than 70% and R² of 0.94). A random forest algorithm trained using the up-sampling UAV infestation map reference and corresponding Radarsat-2 pixel values was then used to produce the Radarsat-2 infestation map, resulting in an overall accuracy of 72.57%. Another random forest algorithm trained using the Radarsat-2 infestation pixels with moderate and high severity (i.e., an infestation severity of larger than 0.25, where the value was empirically set based on a trade-off between classification accuracy and infection detectability) and corresponding Sentinel-1 pixel values was subsequently used to predict the Sentinel-1 infestation map, resulting in an overall accuracy of 87.63%, where the validation data are Radarsat-2 references rather than UAV references. The Sentinel-1 map was also validated by independent UAV surveys, with an overall accuracy of 76.30% and a Kappa coefficient of 0.45. We found that the expanded training samples by the integration of UAV and Radarsat-2 strengthened the medium-resolution Sentinel-1-based prediction model of PWD. This study demonstrates that the proposed method enables effective PWN infestation mapping over multiple scales.

Concealed object detection in millimeter wave (MMW) images has gained significant attention in the realm of public safety, primarily due to its distinctive advantages of non-hazardous and non-contact operation. However, this undertaking confronts substantial challenges in practical applications, owing to the inherent limitations of low imaging resolution, small concealed object size, intricate environmental noise, and the need for real-time performance. In this study, we propose Swin-YOLO, an innovative single-stage detection model built upon transformer layers. Our approach encompasses several key contributions. Firstly, the integration of Local Perception Swin Transform Layers (LPST Layers) enhanced the network’s capability to acquire contextual information and local awareness. Secondly, we introduced a novel feature fusion layer and a specialized prediction head for detecting small targets, effectively leveraging the network’s shallow feature information. Lastly, a coordinate attention (CA) module was seamlessly incorporated between the neck network and the detection head, augmenting the network’s sensitivity towards critical regions of small objects. To validate the efficacy and feasibility of our proposed method, we created a new MMW dataset containing a large number of small concealed objects and conducted comprehensive experiments to evaluate the effectiveness of overall and partial improvements, as well as computational efficiency. The results demonstrated a remarkable 4.7% improvement in the mean Average Precision (mAP) for Swin-YOLO compared with the YOLOv5 baseline. Moreover, when compared with other enhanced transformer-based models, Swin-YOLO exhibited a superior accuracy and the fastest inference speed. The proposed model showcases enhanced performance and holds promise for advancing the capabilities of real-world applications in public safety domains.

Remote sensing image object detection is a challenging task in the field of computer vision due to the complex backgrounds and diverse arrangements of targets in remote sensing images, forming intricate scenes. To overcome this challenge, existing object detection models adopt rotated target detection methods. However, these methods often lead to a loss of semantic information during feature extraction, specifically regarding the insufficient consideration of element correlations. To solve this problem, this research introduces a novel attention module (EuPea) designed to effectively capture inter-elemental information in feature maps and generate more powerful feature maps for use in neural networks. In the EuPea attention mechanism, we integrate distance information and Pearson correlation coefficient information between elements in the feature map. Experimental results show that using either type of information individually can improve network performance, but their combination has a stronger effect, producing an attention-weighted feature map. This improvement effectively enhances the object detection performance of the model, enabling it to better comprehend information in remote sensing images. Concurrently, this also improves missed detections and false alarms in object detection. Experimental results obtained on the DOTA, NWPU VHR-10, and DIOR datasets indicate that, compared with baseline RCNN models, our approach achieves respective improvements of 1.0%, 2.4%, and 1.8% in mean average precision (mAP).

In recent years, dramatic progress in object detection in remote sensing images has been made due to the rapid development of convolutional neural networks (CNNs). However, most existing methods solely pay attention to training a suitable network model to extract more powerful features in order to solve the problem of false detections and missed detections caused by background complexity, various scales, and the appearance of the object. To open up new paths, we consider embedding knowledge into geospatial object detection. As a result, we put forward a method of digitizing knowledge and embedding knowledge into detection. Specifically, we first analyze the training set and then transform the probability into a knowledge factor according to an analysis using an improved version of the method used in existing work. With a knowledge matrix consisting of knowledge factors, the Knowledge Inference Module (KIM) optimizes the classification in which the residual structure is introduced to avoid performance degradation. Extensive experiments are conducted on two public remote sensing image data sets, namely DOTA and DIOR. The experimental results prove that the proposed method is able to reduce some false detections and missed detections and obtains a higher mean average precision (mAP) performance than the baseline method.