With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation and exhibited faster and more accurate performance. In this paper, we propose an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism. We first selected regions using attention coefficients computed by the attention gate and contextual information. Second, a dual attention decoding module consisting of spatial attention and channel attention was used to capture the spatial correlation between features and improve segmentation performance. The combination of the three attentional mechanisms helped the network to focus on a more relevant field of view of the target. The proposed model was evaluated using three datasets, ISIC-2016, ISIC-2017, and PH2. The experimental results demonstrated the effectiveness of our method with strong robustness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.

Time difference of arrival (TDoA) based on a group of sensor nodes with known locations has been widely used to locate targets. Two-step weighted least squares (TSWLS), constrained weighted least squares (CWLS), and Newton–Raphson (NR) iteration are commonly used passive location methods, among which the initial position is needed and the complexity is high. This paper proposes a hybrid firefly algorithm (hybrid-FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy. The WLS algorithm is performed first, the result of which is used to restrict the search region for the FA method. Simulations showed that the hybrid-FA method required far fewer iterations than the FA method alone to achieve the same accuracy. Additionally, two experiments were conducted to compare the results of hybrid-FA with other methods. The findings indicated that the root-mean-square error (RMSE) and mean distance error of the hybrid-FA method were lower than that of the NR, TSWLS, and genetic algorithm (GA). On the whole, the hybrid-FA outperformed the NR, TSWLS, and GA for TDoA measurement.

The detection of small infrared targets lacking texture and shape information in the presence of complex background clutter is a challenge that has attracted considerable research attention in recent years. Typical deep learning-based target detection methods are designed with deeper network structures, which may lose targets in the deeper layers and cannot directly be used for small infrared target detection. Therefore, we designed the attention fusion feature pyramid network (AFFPN) specifically for small infrared target detection. Specifically, it consists of feature extraction and feature fusion modules. In the feature extraction stage, the global contextual prior information of small targets is first considered in the deep layer of the network using the atrous spatial pyramid pooling module. Subsequently, the spatial location and semantic information features of small infrared targets in the shallow and deep layers are adaptively enhanced by the designed attention fusion module to improve the feature representation capability of the network for targets. Finally, high-performance detection is achieved through the multilayer feature fusion mechanism. Moreover, we performed a comprehensive ablation study to evaluate the effectiveness of each component. The results demonstrate that the proposed method performs better than state-of-the-art methods on a publicly available dataset. Furthermore, AFFPN was deployed on an NVIDIA Jetson AGX Xavier development board and achieved real-time target detection, further advancing practical research and applications in the field of unmanned aerial vehicle infrared search and tracking.

The detection of small infrared targets with a low signal-to-noise ratios and contrasts in noisy and cluttered backgrounds is challenging and therefore a domain of active research. Traditional methods result in a large number of false alarms and missed detections. In the case of convolutional neural network-based methods, it may not be possible to identify deep small targets, or the details of the target’s edge contours may not be appropriately considered. Therefore, this paper proposes MSAFFNet to perform infrared small target detection based on an encoder-decoder framework. In the encoder stage, small target features are extracted using a resnet-20 backbone network, and the global contextual features of small targets are extracted using an atrous spatial pyramid pooling module. In the decoding stage, a dual-attention module is used to selectively enhance the spatial details of the target at the shallow level and representative features of the semantic information at the deep level. Multi-scale feature maps are then concatenated to achieve superior feature fusion. Additionally, multi-scale labels are constructed to focus on the details of the target contour and internal features based on edge information and an internal feature aggregation module. Experiments conducted on the NUAA-SIRST, NUDT-SIRST and XDU-SIRST datasets revealed that the proposed approach outperforms the representative methods and achieves an improved detection performance.

The detection of small infrared targets with a low signal-to-noise ratio and low contrast in high-noise backgrounds is challenging due to the lack of spatial features of the targets and the scarcity of real-world datasets. Most existing methods are based on single-frame images, which are prone to numerous false alarms and missed detections. This paper proposes ST-Trans that provides an efficient end-to-end solution for the detection of small infrared targets in the complex context of sequential images. First, the detection of small infrared targets in complex backgrounds relying only on a single image has been significantly difficult due to the lack of available spatial features. The temporal and motion information of the sequence image was found to effectively improve target detection performance. Therefore, we used the C2FDark backbone to learn the spatial features associated with small targets, and the spatial-temporal transformer module to learn the spatiotemporal dependencies between successive frames of small infrared targets. This improved the detection performance in challenging scenes. Second, due to the lack of publicly available infrared small target sequence datasets for training, we annotated a set of small infrared targets for challenging scenes and published them as the sequential infrared small target detection (SIRSTD) dataset. Finally, we performed extensive ablation experiments on the SIRSTD dataset and compared its performance with that of state-of-the-art methods to demonstrate the superiority of the proposed method. The results revealed that ST-Trans outperformed other models and can effectively improve the detection performance for small infrared targets. The SIRSTD dataset is available at https://github.com/aurora-sea/SIRSTD.