In the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Therefore, a novel vision-based autonomous landing navigation scheme is proposed for night-time autonomous landing of fixed-wing UAV. Firstly, due to the difficulty of detecting the runway caused by the low-light image, a strategy of visible and infrared image fusion is adopted. The objective functions of the fused and visible image, and the fused and infrared image, are established. Then, the fusion problem is transformed into the optimal situation of the objective function, and the optimal solution is realized by gradient descent schemes to obtain the fused image. Secondly, to improve the performance of detecting the runway from the enhanced image, a runway detection algorithm based on an improved Faster region-based convolutional neural network (Faster R-CNN) is proposed. The runway ground-truth box of the dataset is statistically analyzed, and the size and number of anchors in line with the runway detection background are redesigned based on the analysis results. Finally, a relative attitude and position estimation method for the UAV with respect to the landing runway is proposed. New coordinate reference systems are established, six landing parameters, such as three attitude and three positions, are further calculated by Orthogonal Iteration (OI). Simulation results reveal that the proposed algorithm can achieve 1.85% improvement of AP on runway detection, and the reprojection error of rotation and translation for pose estimation are $0.{675}^{\circ }$ and 0.581%, respectively.

Aiming at the problem that obstacle avoidance of unmanned aerial vehicles (UAVs) cannot effectively detect obstacles under low illumination, this research proposes an enhancement algorithm for low-light airborne images, which is based on the camera response model and Retinex theory. Firstly, the mathematical model of low-illumination image enhancement is established, and the relationship between the camera response function (CRF) and brightness transfer function (BTF) is constructed by a common parameter equation. Secondly, to solve the problem that the enhancement algorithm using the camera response model will lead to blurred image details, Retinex theory is introduced into the camera response model to design an enhancement algorithm framework suitable for UAV obstacle avoidance. Thirdly, to shorten the time consumption of the algorithm, an acceleration solver is adopted to calculate the illumination map, and the exposure matrix is further calculated via the illumination map. Additionally, the maximum exposure value is set for low signal-to-noise ratio (SNR) pixels to suppress noise. Finally, a camera response model and exposure matrix are used to adjust the low-light image to obtain an enhanced image. The enhancement experiment for the constructed dataset shows that the proposed algorithm can significantly enhance the brightness of low-illumination images, and is superior to other similar available algorithms in quantitative evaluation metrics. Compared with the illumination enhancement algorithm based on infrared and visible image fusion, the proposed algorithm can achieve illumination enhancement without introducing additional airborne sensors. The obstacle object detection experiment shows that the proposed algorithm can increase the AP (average precision) value by 0.556.