In the intelligent inspection of furniture boards, wood debris generated during manufacturing can interfere with the imaging process. This leads to burr disturbances at the corners of the boards. Existing corner detection methods exhibit lower detection accuracy under the interference of these disturbances. To overcome this issue, this paper presents a corner detection algorithm tailored for furniture boards that incorporates the Random Sample Consensus (RANSAC)algorithm and line fitting techniques based on the Huber loss function. To enhance detection efficiency, our algorithm initially identifies horizontal and vertical edges near a corner. This preliminary step facilitates subsequent corner detection. This study introduces a test dataset for evaluating the accuracy and efficiency of algorithms. A battery of comparative experiments, including benchmarks with conventional methods, were conducted. The results demonstrate that our algorithm significantly enhances the efficiency and robustness of corner detection under a variety of complex burr interference conditions.
In order to satisfy the sensing requirement for low-concentration or even trace detection using Terahertz (THz) spectroscopy, a new metamaterial sensor (MS) based on a double-opening elliptical ring array is designed by electromagnetic simulations. After optimization of the MS structure, the proposed MS has a strong resonant peak absorption peak around 2.853 THz and is entitled with high-Q and high-sensitivity simultaneously. The Q-value of the designed MS can reach to 385.0 and the sensitivity can reach to 371.5 GHz/RIU for a dielectric analyte with a thickness of 30 μm and it still can remain 40.0 GHz/RIU for a very thin analyte with a thickness of 1 μm. These results indicate that the designed MS has good sensing performance and can potentially be applied to high-sensitive detection of low-concentration or even trace samples.
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