In order to solve the difficult problem of unmanned air vehicle(UAV) target detection in visible light images under complex sky background, this paper proposes a UAV target detection method based on frequency domain transform. First, the B-channel in the image LAB space is used to extract the sky and cloud boundary images, and then the image feature channel is used to construct a quaternion function. Secondly, Fourier transform is performed on the quaternion function to extract the amplitude spectrum and phase spectrum, then the amplitude spectrum image is subjected to multi-scale decomposition using wavelet transform in the frequency domain, the amplitude spectrum image of each scale and the phase spectrum image are combined by inverse Fourier transform, and the evaluation function is used to obtain the best scale image. Finally, the best-scale image and the boundary image are normalized to make a difference to obtain the final detection result. Experimental results show that the algorithm can effectively detect UAV targets under complex cloud background.
In this paper, we mainly studied how to calculate the energy values of stars received by ground optical systems. Then according to the characteristics of the radiation spectrum of stars and the radiation spectrum of sky background light, we analyzed and selected the observation spectrum of the image-forming system. We also studied the influence of optical system design parameters, such as field of view, focal length and aperture, on detection capability. At the same time, we analyzed and calculate the limitation of the detector's dark current, readout noise and other noise factors on the ability of receiving stars. The significance of these studies is that we provide an effective theoretical basis for designing and improving ground-based photoelectric detection system for star observation during the daytime. In addition, we used digital image processing technology to process the existing observation images and improve the quality of the image. We provide several algorithms for extracting small targets in strong background. We use threshold segmentation, morphological filtering and other methods to improve the signal-to-noise ratio of the image and then improve the detection ability of the system again. According to the simulation, the target extraction accuracy can reach 1/10 pixels when the target imaging size is 4 pixels and the signal-to-noise ratio is less than 5. Improving the detection ability of photoelectric detection system, detecting more available stars and obtaining their relative position information are the important basis for star map matching and the estimation of targets’ position and gesture.
A high pumping-power fiber combiner for backward pumping configurations is fabricated and demonstrated by manufacturing process refinement. The pump power handling capability of every pump fiber can extend to 600 W, corresponding to the average pump coupling efficiency of 94.83%. Totally, 2.67-kW output power with the beam quality factor M2 of 1.41 was obtained, using this combiner in the fiber amplifier experimental setup. In addition, the temperature of the splicing region was less than 50.0°C in the designed combiner under the action of circulating cooling water. The experimental results prove that the designed combiner is a promising integrated all-fiber device for multikilowatt continuous-wave fiber laser with excellent beam quality.
In order to increase the speed controlling accuracy of fast steering mirror (FSM) for image motion compensation and thus to increase the definition of picture taken by moving camera, active disturbance rejection control (ADRC) is designed. First, mathematical model of FSM driven by voice coil motor (VCM) is established. Next, ADRC algorithm and its simplified form in actual application are clarified. Finally, simulation research for controlled object is made. The result is compared to control effect of PID. Simulation curves demonstrate that the settling time of ADRC is 6 ms and the bandwidth of system attains 102.2 Hz, which are nearly the same as those of PID. When the error is very small, it can converge to zero at a faster rate if ADRC is used. When the same range disturbance is given to system, the relative error of ADRC reaches 0.050%, which is about 42% of that of PID.
The detection of small infrared target has always been a crucial but difficult issue in infrared target detection. An
effective method using adaptive Top-Hat operator in morphology is proposed to detect small infrared target in
complicated background. Firstly, adaptive Top-Hat operator is employed to suppress large areas of interference in
complicated background. However, the gray of target is attenuated in the image after Top-Hat operation, which is
disadvantageous to segment target area. Therefore, on the condition that small infrared target is not easy to be
detected accurately, the image processed by morphological filtering is transformed by nonlinear gray stretching in
order to increase the gray of small target. The result of transformation proves that this method not only enhances the
gray of small target but also restrains the interference. In this way, target region can be segmented effectively. The
effectiveness and accuracy of this detecting method is verified by the results of experiments conducted by the author.
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