Reduction of error due to the influence of temperature on the quartz flexible accelerometer without any heating device is a difficult task, and is also a tendency for research and application. In this paper, static and dynamic temperature compensation models are established in order to reduce the temperature influence on accelerometer measurement accuracy. Combined with the experiment data, the relationship between the accelerometer output accuracy, temperature and the magnitude of acceleration is analyzed. The data collected from the temperature experiment show that output value of the accelerometer varies with temperature. The method of uniaxial quadrature experiment is adopted and the accelerometer output value is gauged at temperature ranging from −20• C to 50• C. Having used the static and the dynamic temperature compensation models, the accelerometer temperature error compensation experiment is conducted and the compensated errors by the two models are analyzed. The result shows that the compensated value meets the technical requirements. Two technical indicators, the zero bias K0 and the scaling factor K1, which are used to measure the degree of accelerometers, are both improved and their fluctuation ranges are reduced.

An adaptive low-illumination image enhancement algorithm based on the weighted least squares optimization is proposed to solve the difficulty of detailed feature recognition in low-illumination images that collected by visible light imaging equipment. First, the image is converted from RGB channel to LAB channel. Second, we use an edge-preserving smoothing operator based on the weighted least squares optimization to coarsen smooth base layer and extract multi-scale details in brightness channel. Then, an adaptive weight is proposed and applied to the weighted fusion of smooth base and detail features. Finally, the Retinex enhancement is performed to obtain a ultimate enhanced image. Experiments result show that the image enhanced by this method has suitable visual brightness and clear details. In terms of objective indicators, it has good and stable performance in NIQE, TMQI, and information entropy.

The accurate detection of satellite components based on optical images can provide data support for aerospace missions such as pointing and tracking between satellites. However, the traditional target detection method is inefficient when performing calculations and has a low detection precision, especially when the attitude of the satellite and illumination conditions change considerably. To enable the precise detection of satellite components, we analyse the imaging characteristics of a satellite in space and propose a method to detect the satellite components. This approach is based on a regional-based convolutional neural network (R-CNN), and it can enable the accurate detection of various satellite components by using optical images. First, on the basis of the Mask R-CNN, we combine the DenseNet, ResNet, and FPN to construct a new feature extraction structure and obtain the R-CNN based satellite-component-detection model (RSD). The feature maps are extracted and concatenated at a deeper multiscale level, and the feature propagation between each layer is enhanced by providing a dense connection. Next, an information-rich satellite dataset is constructed, which is composed of images of various kinds of satellites from various perspectives and orbital positions. The detection model is trained and optimized on the constructed dataset to obtain the satellite component detection model. Finally, the proposed RSD model and original Mask R-CNN are tested on the same established test set. The experimental results show that the proposed detection model has higher precision, recall rate, and F1 score. Therefore, the proposed approach can effectively detect satellite components, based on optical images.

A new image fusion scheme based on wavelet transform is proposed in this paper. Firstly, the image is decomposed into high-frequency images and lowfrequency images with wavelet transform, then the spatial frequency and the contrast of the low-frequency image are measured to determine the fused lowfrequency image, and to the high-frequency image, we select the high-frequency coefficient based on the absolute value maximum principal and verify the consistency of these coefficients. Finally, the image can be reconstructed with Mallat algorithm. The experimental results show that the scheme can preserve all useful information from primitive images and the clarity and the contrast of the fused image are improved. The presented scheme is verified to be effective for the image fusion.

At present, in terms of clinical temperature measurement in China, most hospitals use traditional mercury thermometers to obtain patient temperature data through human readings and records. In order to improve the work efficiency of medical staff and reduce the frequency of their contact with patients, machine vision is adopted. A scale recognition method of non-electronic thermometer based on image features is proposed. The reading data of clinical thermometers is calculated through pixels. The automatic reading record of the clinical thermometer is realized, and the relative error does not exceed 0.25% in the temperature range of 35.5 to 37.5°C, which has certain practical significance for the research in the field of intelligent medical treatment.