Search Results (2)

The laser beam mode affects the power density distribution on the irradiated target, directly influencing the product quality in laser processing, especially the hole quality in laser drilling. The Gaussian beam shape, Mexican-Hat beam shape, Double-Hump beam shape, and Top-Hat beam shape are four typical laser beam modes used as a laser heat source and introduced into our proficient laser-drilling model, which involves complex physical phenomena such as heat and mass transfer, solid/liquid/gas phase changes, and two-phase flow. Simulations were conducted on an aluminum target, and the accuracy was verified using experimental data. The results of the simulations for the fundamental understanding of this laser–material interaction are presented in this paper; in particular, the hole shape, including the depth–diameter ratio and the angle of the cone, as well as spatter phenomena and, thus, the formed recast layer, are compared and analyzed in detail in this paper. This study can provide a reference for the optimization of the laser-drilling process, especially the selection of laser beam mode. Full article

Although infrared small target detection has been broadly used in airborne early warning, infrared guidance, surveillance and tracking, it is still an open issue due to the low signal-to-noise ratio, less texture information, background clutters, and so on. Aiming to detect a small target in an infrared image with complex background clutters, this paper carefully studies the characteristics of a target in an IR image filtered by the difference of Gaussian filter, concluding that the intensity of the adjacent region around a small infrared target roughly has a Mexican-hat distribution. Based on such a conclusion, a raw infrared image is sequentially processed with the modified top-hat transformation and the difference of Gaussian filter. Then, the adjacent region around each pixel in the processed image is radially divided into three sub-regions. Next, the pixels that distribute as the Mexican-hat are determined as the candidates of targets. Finally, a real small target is segmented out by locating the pixel with the maximum intensity. Our experimental results on both real-world and synthetic infrared images show that the proposed method is so effective in enhancing small targets that target detection gets very easy. Our method achieves true detection rates of 0.9900 and 0.9688 for sequence 1 and sequence 2, respectively, and the false detection rates of 0.0100 and 0 for those two sequences, which are superior over both conventional detectors and state-of-the-art detectors. Moreover, our method runs at 1.8527 and 0.8690 s per frame for sequence 1 and sequence 2, respectively, which is faster than RLCM, LIG, Max–Median, Max–Mean. Full article