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).

Vision-inertial navigation offers a promising solution for aircraft to estimate ego-motion accurately in environments devoid of Global Navigation Satellite System (GNSS). However, existing approaches have limited adaptability for fixed-wing aircraft with high maneuverability and insufficient visual features, problems of low accuracy and subpar real-time arise. This paper introduces a novel vision-inertial heterogeneous data fusion methodology, aiming to enhance the navigation accuracy and computational efficiency of fixed-wing aircraft landing navigation. The visual front-end of the system extracts multi-scale infrared runway features and computes geo-reference runway image as observation. The infrared runway features are recognized efficiently and robustly by a lightweight end-to-end neural network from blurry infrared images, and the geo-reference runway is generated through projection of the runway’s prior geographical information and prior pose. The fusion back-end of the navigation system is the Covariance Feedback Control based Cubature Kalman Filter (CFC-CKF) framework, which tightly integrates visual observations and inertial measurements for zero-drift pose estimation and curbs the effect of inaccurate kinematic noise statistics. Finally, real flight experiments demonstrate that the algorithm can estimate the pose at a frequency of 100 Hz and fulfill the navigation accuracy requirements for high-speed landing of fixed-wing aircraft.

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.

This paper introduces VI-DynaNet, a vision–inertial dynamical network developed for real-time pose estimation of fixed-wing aircraft during the critical landing phase, particularly in environments where global navigation satellite systems are unavailable or unreliable. The visual front-end of VI-DynaNet generates geo-referenced runway images using geographic data, while a dedicated neural network predicts the pixel coordinates of runway corners. The vision–inertial fusion back-end employs a deep learning-enhanced error-state extended Kalman filter that learns the nonlinear mapping between observed runway images and their geo-referenced counterparts. VI-DynaNet delivers high-frequency, drift-free pose estimates with minimal computational overhead. Evaluation using real flight data demonstrates that the method achieves pose estimation at 100 Hz, satisfying the stringent precision requirements of fixed-wing aircraft landing operations.

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.