The differences in material properties of various common recyclable garbage will be directly mapped to the difference in spectral characteristics. While acquiring the spectral information of the garbage, the hyperspectral imaging technology can obtain the spatial information of the garbage, and realize the rapid detection and classification of garbage by using the method of "spectral-spatial" .The classification model is established combined the spectral characteristics under the sample feature space with CNN (convolutional neural network) machine learning algorithm, and then the classification model is trained and optimized by using database training sample set. Finally, 92.10% classification accuracy is achieved after testing the sample set.
In order to solve the weakness of traditional vertical target coordinate measurement, such as complex structure, inconvenient debugging and difficult to form a large area target, a method based on intersection laser screens was proposed. The effective area of was constituted by the intersection of two fan-shaped laser screens. Photodiode (PD) array was used to measure the intensity of laser light. When the projectile passes through the laser screens, the corresponding PDs receive signal. After signal processing and adaptive threshold comparator circuit processing, the signal is transmitted to PC to calculate the projectile target coordinate. The calculation method of feature point and the calculation process of coordinate were described in detail. Error distribution in effective area was analyzed. Experiment was carried out by using simulating projectile and the principle prototype whose effective target area is 1m×1m. The result shows that the precision of location measurement is up to 2mm. The method has the advantages of simple structure, convenient debugging. It is easy to construct a larger area target, and the maximum effective area can reach to 6m×6m.
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