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For the recognition of armored targets in complex battlefield environments, how to reduce missed and false alarms while achieving real-time is an urgent issue. To this end, the GCD-YOLOv5 algorithm is innovatively proposed. Firstly, array lidar is used to acquire the armor target data. Secondly, the armor target data is expanded with an improved GAN(Generative Adversarial Network) to increase the diversity of training data. Afterward, the expanded dataset is fed into the GCD-YOLv5(You Only Look Once) for training. And the GCD-YOLOv5 is reflected in the following aspects. Firstly, the CBAM(Convolutional Block Attention Module) and the multi-scale feature fusion are added to improve the feature extraction capability and detection efficiency, increasing the recognition capability of small and obscured targets. Secondly, combining with DETR(Detection Transformer) to lighten YOLOv5 to achieve the real-time requirement. Thirdly, the YOLOv5 loss function and prediction box filtering method are improved to increase the detection accuracy and the confidence of the detection boxes. The experimental results show that the GCD-YOLOv5 algorithm has higher accuracy and real-time, the mAP(mean Average Precision) can reach 99.7%, and fps is 68.56% higher compared to YOLOv5, which significantly improves the recognition capability of armored targets in complex battlefield environments.

The noise suppression of high-resolution range profile (HRRP) is a prerequisite for Geiger-mode of avalanche photodiodes (GM-APD) lidar to achieve precise sensing. However, it is difficult to balance the suppression effect and the integrity of detailed information. Considering this problem, we propose a Bayesian network-based improved loop filter (BNILF) for abnormal noise suppression. Based on the ILF, the Bayesian non-average local filtering model is established to calculate a distance of Pearson distance, which gives the criterion of noise judgment. Furthermore, the block preselection is used to accelerate identify abnormal noise and complete range profile noise suppression. To evaluate the performance of this algorithm, simulation and physical system experiments are carried out. The results show that the proposed algorithm has a better noise suppression effect and a higher detailed information protection ability in comparison with the existing typical approaches.