The supply chain of agricultural products is intricately linked to the daily lives of people. In light of rising import and export quantities, the need for a prompt and efficient inspection system has become increasingly pressing. Without opening baskets and manually sorting, a smart inspection scheme is designed in this work leveraging X-ray images and transformer neural network. Due to its penetrating capabilities, X-ray enables a direct examination of agricultural products within a basket, a task that normal vision devices are unable to accomplish. Taking into account the varying shapes and combinations of agricultural products, we introduce a transformer-based deep neural network for type identification. Additionally, a dataset augmentation process is developed inspired by computed tomography generating 1,6000 X-ray images. Through experiments, the proposed smart inspection scheme is proven to be feasible and works efficiently. The inspection accuracy for both single-type and mixed-type agricultural products on the established dataset exceeds 90%.
A novel position measurement system, the so-called image grating system, is presented in this research paper. It features an adjustable measurement range, flexible standoff distance and in-line measurement capabilities. The developed position measurement system includes an image grating attached to the moving stage as the target feature and a line scan camera as the stationary displacement reader. By observing the position of the target feature in the image and applying subpixel image registration, the position of the moving stage can be determined. In order to improve the measurement efficiency, the computations for pattern correlation and subpixel registration are performed in the frequency domain. Calibration and error correction methods are also developed to compensate for the measurement error caused by optical distortion. Experimental data confirms the capability of the image grating technology of ±0.2 μm measurement accuracy within 25 mm measurement range. By applying different optics, the standoff distance and the measurement range can be customized for different precision measurement applications.
X-ray computed tomography (CT) is a non-destructive approach to verify internal features of various industrial components built by additive manufacturing (AM) or other processing methods. However, the measurement results was highly impacted by numerous factors. In this study, DoE (Design of Experiments) was conducted to statistically study impacts of error source of X-ray CT metrology; optimal settings were recommended for different internal geometrical features. Measurement comparison between X-ray CT and CMM (Coordinate Measuring Machine) is also provided in this paper to analyze the principle difference of these two measurement technology.
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