Laser interference can interfere with target detection systems to achieve the goal of protecting the target. For example, it can obscure the target and change information such as brightness and contrast in the surrounding region of the interference zone. Therefore, it is necessary to analyze the impact of laser interference on target detection algorithms and evaluate the anti-interference performance of the algorithm. This paper analyzes the impact of laser interference on two target detection algorithms, Faster-RCNN and YOLO-V3, from two perspectives: target occlusion rate and target similarity. Then, a target-oriented method for dividing the effective region of laser interference (TODERLI) is proposed. The effectiveness of the algorithm is verified through experiments.
The identification of launch event based on infrared image processing is an important machine learning technology for space-based early warning system. Due to the long-range detection and short duration of the powered phase of trajectory, launch event represents as a point target in the infrared image. Therefore, the main challenge is to address the issue of distinguishing the different types of infrared point target. To tackle this problem, we propose a novel method for recognizing images of rocket exhaust flame point targets at different temperatures. To validate our approach, we conducted experimental validation using simulated detecting images obtained from high-orbit infrared early warning satellites. These images are generated based on the EO/IR module in STK software. The experimental results verify the effectiveness of our proposed method in solving the challenge of infrared point target recognition.
The task of classifying small objects is still challenging for current deep learning classification models [such as convolutional neural networks (CNNs) and vision transformers (ViTs)]. We believe that these algorithms are not designed specifically for small targets, so their feature extraction abilities for small targets are insufficient. To improve the classification capabilities of CNN-based and ViT-based classification models for small objects, two multidomain feature fusion (MDFF) frameworks are proposed to increase the amount of feature information derived from images and they are called MDFF-ConvMixer and MDFF-ViT. Compared with the basic model, the uniquely added design includes frequency domain feature extraction and MDFF processes. In the frequency domain feature extraction part, the input image is first transformed into a frequency domain form through discrete cosine transform (DCT) transformation and then a three-dimensional matrix containing the frequency domain information is obtained via channel splicing and reshaping. In the MDFF part, MDFF-ConvMixer splices the spatial and frequency domain features by channel, whereas MDFF-ViT uses a cross-attention mechanism to fuse the spatial and frequency domain features. When targeting small target classification tasks, these two frameworks obviously improve the utilized classification algorithm. On the DOTA dataset and the CIFAR10 dataset with two downsampling operations, the accuracies of MDFF-ConvMixer relative to ConvMixer increase from 87.82% and 62.14% to 90.14% and 66.00%, respectively, and the accuracies of MDFF-ViT relative to the ViT increase from 79.22% and 36.2% to 88.15% and 59.23%, respectively.
Most of the laser interfered image quality assessment algorithms need to know the reference images or partial information of reference images. However, in practical application, the reference image or its related information is difficult to obtain, which makes the application scenario of laser interference image quality evaluation algorithm is greatly limited. To solve this problem, this paper starts with the prediction processing of the obscured information and improves the Markov Random Field estimation algorithm (MRF) to realize the real-time estimation of the obscured area information. Then, proposes a non-reference image quality assessment method based on occlusion area information estimation and natural scene statistics (IENSS), which analyzes the statistical characteristics of laser interfered images in natural scenes. The model is trained by machine learning. Finally, simulation experiments are carried out to verify the effectiveness of the proposed method.
Space-based remote sensing is an important way of detecting many types of land targets. For the purpose of taking cover, land targets have a strong demand for avoiding space-based remote sensing reconnaissance. In practice, space-based reconnaissance will produce huge data, which are unbearable for human-beings. Therefore, the data processing must rely on artificial intelligence technology such as deep neural network. Many previous works show that the existing intelligent target detection algorithm based on deep neural network will be affected by perturbations. Firstly, this paper establishes a target detection method based on the Faster RCNN framework, and then three types of disturbances methods are studied to help the mobile radar to counter the typical space-based artificial intelligence detection algorithm. The simulation results show that the three types of disturbances methods can fool the typical target detection technology based on deep neural network.
When the off-axis two reflection optical system is applied to the infrared system, it faces the contradiction between Wide FOV and small F#. The off-axis three reflection system can increase field of view in the symmetrical direction but difficult in the asymmetric direction. This paper explores a method to solve this problem. First, a three-mirror reflection system is designed with off-axis field of view and aperture according to requirements of field of view and F# in the symmetrical direction. The field of view in the asymmetric direction is set at about 1°, and the aperture diaphragm is placed in front of the image plane to form Lyot Stop. Then the main mirror is planarized and removed that the secondary mirror and the tertiary mirror can form an Anti-telephoto structure, the field of view is increased in the asymmetric direction. Finally, the dimensional constraints are established to avoid occlusion and adjust and optimize this system. According to this method, a double off-axis two reflection optical system suitable for infrared imaging is designed with the f-number 1.6, the field of view 7°×7°, the distortion less than 3% and the imaging quality close to the diffraction limit.
Automatic facial expression recognition has been one of the research hotspots in the area of computer vision for nearly ten years. During the decade, many state-of-the-art methods have been proposed which perform very high accurate rate based on the face images without any interference. Nowadays, many researchers begin to challenge the task of classifying the facial expression images with corruptions and occlusions and the Sparse Representation based Classification framework has been wildly used because it can robust to the corruptions and occlusions. Therefore, this paper proposed a novel facial expression recognition method based on Weber local descriptor (WLD) and Sparse representation. The method includes three parts: firstly the face images are divided into many local patches, and then the WLD histograms of each patch are extracted, finally all the WLD histograms features are composed into a vector and combined with SRC to classify the facial expressions. The experiment results on the Cohn-Kanade database show that the proposed method is robust to occlusions and corruptions.
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