Magnetic anomaly detection (MAD) technology has been widely used in geological exploration, unexploded ordnance exploration, wreck salvage, and other fields because of its attractive advantages such as all-weather implementation, high concealment, and signal propagation in water and soil. The most classic method in MAD is orthonormal basis functions (OBFs) detection which can effectively detect the magnetic target in a comparatively low signal-to-noise ratio (SNR) environment. In this article, attracted by the excellent properties of magnetic gradient tensor (MGT) invariant, the ${{\mathrm{I}}_{1}}$ -OBF and ${{\mathrm{I}}_{2}}$ -OBF methods are developed. Considering strong dependence of the ${{\mathrm{I}}_{2}}$ -OBF method on the angle between magnetic moment vector and displacement vector, and moderate detection performance of the ${{\mathrm{I}}_{1}}$ -OBF method, an effective invariant-OBF method through adaptively integrating search energy of the ${{\mathrm{I}}_{1}}$ -OBF and ${{\mathrm{I}}_{2}}$ -OBF methods is proposed. Simulations and field experiments witnessed a great improvement in the SNR of the proposed method from the perspective of signal energy. It is proved that the proposed method can stably produce promising results even under the shaking-platform condition or the specific geometric relationship between the magnetic moment vector and the displacement vector.

It has been difficult to achieve a suitable balance between effectiveness and efficiency in lightweight semantic segmentation networks in recent years. The goal of this work is to present an efficient and reliable semantic segmentation method called EBUNet, which is aimed at achieving a favorable trade-off between inference speed and prediction accuracy. Initially, we develop an Efficient Bottleneck Unit (EBU) that employs depth-wise convolution and depth-wise dilated convolution to obtain adequate features with moderate computation costs. Then, we developed a novel Image Partition Attention Module (IPAM), which divides feature maps into subregions and generates attention weights based on them. As a third step, we developed a novel lightweight attention decoder with which to retrieve spatial information effectively. Extensive experiments show that our EBUNet achieves 73.4% mIou and 152 FPS on the Cityscapes dataset and 72.2% mIoU and 147 FPS on the Camvid dataset with only 1.57 M parameters. The results of the experiment confirm that the proposed model is capable of making a decent trade-off in terms of accuracy, inference, and model size. The source code of our EBUNet is available at (https://github.com/Skybird1101/EBUNet).

The runway detection algorithm for fixed-wing aircraft is a hot topic in the field of aircraft visual navigation. High accuracy, high fault tolerance, and lightweight design are the core requirements in the domain of runway feature detection. This paper aims to address these needs by proposing a lightweight runway feature detection algorithm named YOMO-Runwaynet, designed for edge devices. The algorithm features a lightweight network architecture that follows the YOMO inference framework, combining the advantages of YOLO and MobileNetV3 in feature extraction and operational speed. Firstly, a lightweight attention module is introduced into MnasNet, and the improved MobileNetV3 is employed as the backbone network to enhance the feature extraction efficiency. Then, PANet and SPPnet are incorporated to aggregate the features from multiple effective feature layers. Subsequently, to reduce latency and improve efficiency, YOMO-Runwaynet generates a single optimal prediction for each object, eliminating the need for non-maximum suppression (NMS). Finally, experimental results on embedded devices demonstrate that YOMO-Runwaynet achieves a detection accuracy of over 89.5% on the ATD (Aerovista Runway Dataset), with a pixel error rate of less than 0.003 for runway keypoint detection, and an inference speed exceeding 90.9 FPS. These results indicate that the YOMO-Runwaynet algorithm offers high accuracy and real-time performance, providing effective support for the visual navigation of fixed-wing aircraft.

In magnetic surveys, three-axis magnetometers (TAMs) are usually installed on large carriers and the TAM outputs are inevitably subjected to errors from TAM and the carrier (including scale factor, non-orthogonality, bias error, temperature error, soft iron error and hard iron error). Traditional Compensation methods may result in unreliable and unsuccessful solutions when manoeuvres of the vehicle are constrained. Therefore, a compensation method based on regularized inverse problems is proposed. We inspect the performance of the proposed method by conducting simulated and semi-physical experiments. All the results indicate that the proposed calibration method can still achieve high performance in the case of narrow-range attitudes and obtain more accurate magnetic components.

Camera localization approaches often degrade in challenging environments characterized by illumination variations and significant viewpoint changes, presenting critical limitations for fixed-wing aircraft landing applications. To address these challenges, we propose LandNet—a novel absolute camera pose estimation network specifically designed for airborne scenarios. Our framework processes images from forward-looking aircraft cameras to directly predict 6-DoF camera poses, subsequently enabling aircraft pose determination through rigid transformation. As a first step, we design two encoders from Transformer and CNNs to capture complementary spatial–temporal features. Furthermore, a novel Feature Interactive Block (FIB) is employed to fully utilize spatial clues from the CNN encoder and temporal clues from the Transformer encoder. We also introduce a novel Attentional Convtrans Fusion Block (ACFB) to fuse the feature maps from encoder and transformer encoder, which can enhance the image representations to promote the accuracy of the camera pose. Finally, two Multi-Layer Perceptron (MLP) heads are applied to estimate 6-DOF of camera position and orientation, respectively. Thus the estimated position and orientation of our LandNet can be further used to acquire the pose and orientation of the aircraft through the rigid connection between the airborne camera and the aircraft. The experimental results from simulation and real flight data demonstrate the effectiveness of our proposed method.

In recent years, semantic segmentation has been used extensively in a variety of scenarios. It is essential for most practical applications that predictions are accurate and efficient. Toward this end, we present a novel lightweight attention guided semantic segmentation network (LAGNet) aiming for a balance between prediction accuracy and running efficiency. As a first step, we developed the Efficient Inter-attention Bottleneck Module (EIRM) in order to obtain local and contextual information at a lower cost of computation. We then present a novel Image Decomposition Attention Mechanism (IDAM) that refines the feature maps at various stage. Furthermore, we present a novel Decoder named Mutual-Attention Guided Decoder to promote the accuracy of the prediction results, which utilizes attention mechanism to recover the detailed information effectively. The results of extensive experiments on Cityscapes and Camvid datasets show that our model achieves 73.3% and 71.4% mIoU along with 110 and 105 frames per second on the Cityscapes and Camvid datasets, respectively.