Due to the strong scattering of fog and the strong background noise, the signal-to-background ratio (SBR) is extremely low, which severely limits the 3D imaging capability of single-photon detector array through fog. Here, we propose an outdoor three-dimensional imaging algorithm through fog, which can separate signal photons from non-signal photons (scattering and noise photons) with SBR as low as 0.003. This is achieved by using the observation model based on multinomial distribution to compensate for the pile-up, and using dual-Gamma estimation to eliminate non-signal photons. We show that the proposed algorithm enables accurate 3D imaging of 1.4 km in the visibility of 1.7 km. Compared with the traditional algorithms, the target recovery (TR) of the reconstructed image is improved by 20.5%, and the relative average ranging error (RARE) is reduced by 28.2%. It has been successfully demonstrated for targets at different distances and imaging times. This research successfully expands the fog scattering estimation model from indoor to outdoor environment, and improves the weather adaptability of the single-photon detector array.

Aiming at the problems of long-distance night dynamic object detection in military operations, such as difficult imaging of traditional equipment, a small number of object pixels, a large difference in object size at different distances, and poor real-time performance. A long-distance vehicle detection method based on the LIDAR-YOLO network for Gm-APD (Geiger mode avalanche photodiode) lidar image is proposed. This method is improved based on YOLOv5s, which can detect long-distance vehicles and accurately locate motion trajectories in real-time. Firstly, the intensity image I of the lidar is preprocessed and combined with the distance image R, the BC_IIR image is obtained, and the vehicle detection data set is established. Then, according to the obvious vehicle features in the BC_IIR image, the low-complexity backbone network LIDARNet is redesigned, and several attention mechanism modules are added. The network feature extraction ability is enhanced by comparing and selecting the CA attention mechanism module. In the network prediction layer, context-aware feature map fusion is added to improve the detection precision of vehicles at different distances. Next, a GIOU loss is introduced to improve the detection precision of occluded vehicles. Finally, the method can depict the trajectory of the vehicle in 3D space based on distance information. After testing, the network achieves a detection speed of 62.67 frames per second (FPS), recall rate (R) = 98.19%, precision rate (P) = 95.53%, and F1 is 0.9675. The results show that the algorithm has better performance than other methods.

Owing to the weak signals produced by all-fiber coherent lidar systems, when the instability of the local oscillator laser power is greater than the target echo signal, it is difficult to extract a target’s intensity image. In this study, an intensity imaging method for weak signal all-fiber lidars is proposed. First, a phase compensation method is used to correct the position of the heterodyne signal in the time domain to reduce the impact of noise on the positioning heterodyne signal. In addition, an algorithm is proposed to extract the weak echo signal from the corrected heterodyne signal in the time domain to obtain the relative intensity of the echo signal of a single pixel point. Finally, we analyze and verify the proposed imaging method by using false alarm rates, range, intensity accuracy, and the speckle characteristics of the target. The method proposed in this study only requires that the phase of the heterodyne signal be corrected by the proposed numerical method without the need for other optical equipment, thus simplifying the entire system. It is very important to improve the detection sensitivity of coherent lidar remote imaging system.