Owing to the significant application potential of unmanned aerial vehicles (UAVs) and infrared imaging technologies, researchers from different fields have conducted numerous experiments on aerial infrared image processing. To continuously detect small road objects 24 h/day, this study proposes an efficient Rep-style Gaussian–Wasserstein network (ERGW-net) for small road object detection in infrared aerial images. This method aims to resolve problems of small object size, low contrast, few object features, and occlusions. The ERGW-net adopts the advantages of ResNet, Inception net, and YOLOv8 networks to improve object detection efficiency and accuracy by improving the structure of the backbone, neck, and loss function. The ERGW-net was tested on a DroneVehicle dataset with a large sample size and the HIT-UAV dataset with a relatively small sample size. The results show that the detection accuracy of different road targets (e.g., pedestrians, cars, buses, and trucks) is greater than 80%, which is higher than the existing methods.

This paper proposes a framework for unmixing of hyperspectral data that is based on utilizing the scattering transform to extract deep features that are then used within a neural network. Previous research has shown that using the scattering transform combined with a traditional K-nearest neighbors classifier (STFHU) is able to achieve more accurate unmixing results compared to a convolutional neural network (CNN) applied directly to the hyperspectral images. This paper further explores hyperspectral unmixing in limited training data scenarios, which are likely to occur in practical applications where the access to large amounts of labeled training data is not possible. Here, it is proposed to combine the scattering transform with the attention-based residual neural network (ResNet). Experimental results on three HSI datasets demonstrate that this approach provides at least 40% higher unmixing accuracy compared to the previous STFHU and CNN algorithms when using limited training data, ranging from 5% to 30%, are available. The use of the scattering transform for deriving features within the ResNet unmixing system also leads more than 25% improvement when unmixing hyperspectral data contaminated by additive noise.

Enhancing the detection capabilities of marine vessels is crucial for maritime security and intelligence acquisition. However, accurately identifying small ships in complex oceanic environments remains a significant challenge, as these targets are frequently obscured by ocean waves and other disturbances, compromising recognition accuracy and stability. To address this issue, we propose YOLO-ssboat, a novel small-target ship recognition algorithm based on the YOLOv8 framework. YOLO-ssboat integrates the C2f_DCNv3 module to extract fine-grained features of small vessels while mitigating background interference and preserving critical target details. Additionally, it employs a high-resolution feature layer and incorporates a Multi-Scale Weighted Pyramid Network (MSWPN) to enhance feature diversity. The algorithm further leverages an improved multi-attention detection head, Dyhead_v3, to refine the representation of small-target features. To tackle the challenge of wake waves from moving ships obscuring small targets, we introduce a gradient flow mechanism that improves detection efficiency under dynamic conditions. The Tail Wave Detection Method synergistically integrates gradient computation with target detection techniques. Furthermore, adversarial training enhances the network’s robustness and ensures greater stability. Experimental evaluations on the Ship_detection and Vessel datasets demonstrate that YOLO-ssboat outperforms state-of-the-art detection algorithms in both accuracy and stability. Notably, the gradient flow mechanism enriches target feature extraction for moving vessels, thereby improving detection accuracy in wake-disturbed scenarios, while adversarial training further fortifies model resilience. These advancements offer significant implications for the long-range monitoring and detection of maritime vessels, contributing to enhanced situational awareness in expansive oceanic environments.

With the rapid development of UAV and infrared imaging technology, the cost of UAV infrared imaging technology has decreased steadily. Small target detection technology in aerial infrared images has great potential for applications in many fields, especially in the field of traffic surveillance. Because of the low contrast and relatively limited feature information in infrared images compared to visible images, the difficulty involved in small road target detection in infrared aerial images has increased. To solve this problem, this study proposes a feature-enhanced attention and dual-GELAN net (FEADG-net) model. In this network model, the reliability and effectiveness of small target feature extraction is enhanced by a backbone network combined with low-frequency enhancement and a swin transformer. The multi-scale features of the target are fused using a dual-GELAN neck structure, and a detection head with the parameters of the auto-adjusted InnerIoU is constructed to improve the detection accuracy for small infrared targets. The viability of the method was proved using the HIT-UAV dataset and IRTS-AG dataset. According to a comparative experiment, the mAP₅₀ of FEADG-net reached more than 90 percent, which was higher than that of any previous method and it met the real-time requirements. Finally, an ablation experiment was conducted to demonstrate that all three of the modules proposed in the method contributed to the improvement in the detection accuracy. This study not only designs a new algorithm for small road object detection in infrared remote sensing images from UAVs but also provides new ideas for small target detection in remote sensing images for other fields.

Campus security incidents occur from time to time, which seriously affect the public security. In recent years, the rapid development of artificial intelligence has brought technical support for campus intelligent security. In order to quickly recognize and locate dangerous targets on campus, an improved YOLOv3‐Tiny model is proposed for dangerous target detection. Since the biggest advantage of this model is that it can achieve higher precision with very fewer parameters than YOLOv3‐Tiny, it is one of the Tinier‐YOLO models. In this paper, the dangerous targets include dangerous objects and dangerous actions. The main contributions of this work include the following: firstly, the detection of dangerous objects and dangerous actions is integrated into one model, and the model can achieve higher accuracy with fewer parameters. Secondly, to solve the problem of insufficient YOLOv3‐Tiny target detection, a jump‐join repetitious learning (JRL) structure is proposed, combined with the spatial pyramid pooling (SPP), which serves as the new backbone network of YOLOv3‐Tiny and can accelerate the speed of feature extraction while integrating features of different scales. Finally, the soft‐NMS and DIoU‐NMS algorithm are combined to effectively reduce the missing detection when two targets are too close. Experimental tests on self‐made datasets of dangerous targets show that the average MAP value of the JRL‐YOLO algorithm is 85.03%, which increases by 3.22 percent compared with YOLOv3‐Tiny. On the VOC2007 dataset, the proposed method has a 9.29 percent increase in detection accuracy compared to that using YOLOv3‐Tiny and a 2.38 percent increase compared to that employing YOLOv4‐Tiny, respectively. These results all evidence the great improvement in detection accuracy brought by the proposed method. Moreover, when testing the dataset of dangerous targets, the model size of JRL‐YOLO is 5.84 M, which is about one‐fifth of the size of YOLOv3‐Tiny (33.1 M) and one‐third of the size of YOLOv4‐Tiny (22.4 M), separately.

Hyperspectral image (HSI) classification faces challenges such as high dimensionality, spectral redundancy, and difficulty in modeling the coupling between spectral and spatial features. Existing methods fail to fully exploit first-order derivatives and frequency domain information, which limits classification performance. To address these issues, this paper proposes a Differential-Frequency Attention-based Band Selection Transformer (DFAST) for HSI classification. Specifically, a Differential-Frequency Attention-based Band Selection Embedding Module (DFASEmbeddings) is designed to extract original spectral, first-order derivative, and frequency domain features via a multi-branch structure. Learnable band selection attention weights are introduced to adaptively select important bands, capture critical spectral information, and significantly reduce redundancy. A 3D convolution and a spectral–spatial attention mechanism are applied to perform fine-grained modeling of spectral and spatial features, further enhancing the global dependency capture of spectral–spatial features. The embedded features are then input into a cascaded Transformer encoder (SCEncoder) for deep modeling of spectral–spatial coupling characteristics to achieve classification. Additionally, learnable attention weights for band selection are outputted for dimensionality reduction. Experiments on several public hyperspectral datasets demonstrate that the proposed method outperforms existing CNN and Transformer-based approaches in classification performance.