Target detection based on deep learning methods has important applications in the field of remote sensing images target detection. Aiming at the disadvantages of the small target scale for remote sensing images, this paper proposes an improved YOLOv4 algorithm to improve the detection effect of remote sensing images. The experimental results show that the algorithm proposed in this paper has an average accuracy rate of 2.25% higher than that of the original YOLOv4 algorithm. The accuracy of detecting small-scale targets has been significantly improved, and the amount of model parameters has also been reduced compared to the original algorithm.
In the environmental security monitoring application, an optical fiber prewarning system (OFPS) functions not only to locate the intrusion events but also recognize them. As a nonlinear network for recognition, the stochastic configuration network (SCN) is considered a promising method because it does not require setting the network scale beforehand. However, in the specific requirements of the application of OFPS, due to the small feature distance of different intrusion signals to be classified, it is necessary to set a smaller value of error tolerance. However, the side-effect is that meeting the constraint condition faces a challenge. To overcome this, we improve the configuration method of the hidden layer nodes in the SCN network. In the proceeding of the network process, the increment of the hidden layer nodes in each loop is gradually increased, and the space of the corresponding random parameters generated is enlarged. The SCN with variable increments of hidden nodes can adjust the number of hidden nodes added in each loop for continuous construction and obtaining higher classification accuracy. This study has a great significance for the application of SCN in the classification of intrusion signals in OFPS.
KEYWORDS: Unmanned aerial vehicles, Synthetic aperture radar, Error analysis, Motion models, Signal to noise ratio, Systems modeling, Imaging systems, Data modeling, Signal processing, Scattering
Unmanned aerial vehicles (UAV) are a useful supplement to traditional synthetic aperture radar (SAR) platforms. In some cases, UAV-based SAR systems have to fly at low altitude. In this case, range-dependent phase errors due to platform motion affect the imaging quality. To solve the problem of motion compensation, an angle-dependent model and a second-order range-dependent model are introduced into autofocusing by previous researchers, but the first one relies too much on the geometric angle while the latter has limited fitting order for solution. We present a higher order range-dependent model, which can approximate analytical solution. Nevertheless, an increase in the fitting order makes the matrix in this model underdetermined. Based on the theoretical proof, this higher order model can be tackled by exploitation of compressive sensing (CS) theory. A CS reconstruction of higher order fitting coefficients is performed in the experiments, and corresponding performance analysis is given. Finally, the range-dependent phase error is compensated under the condition of low altitude.
To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.
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