A Garlic Sorting Machine Using Multi-layer Perceptron Neural Network
| Autor: | CHIANG, HSU-CHENG, 蔣序承 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 105 The thesis develops an image recognition system that can be installed on the garlic sorting machine to check whether the garlic is damaged. The farmer can process the garlic more accurately and quickly. The garlic machine can reduce the manpower while classifying the garlic. The system utilizes software and hardware design techniques to develop a garlic sorter with a multi-layer perceptron neural network. Firstly, the system uses a personal computer to perform a Multi-Layer Perceptron Neural Network (MLP-NN) training kit written by OpenCV software. The training kit uses self-photographed sample images to generate Gray-Level Co-Occurrence Matrix (GLCM) by a statistical method. Then the GLCM calculates the characteristic values such as entropy and contrast. These values are inputted into the MLP-NN function for training. After that, the weight values generated from training are realized to a Field Programming Gate Array (FPGA) chip. The MLP-NN function is realized with FPGA chip for accelerating the garlic sorting process. In order to achieve low-latency and high-speed system performance, the MLP-NN is realized by a XC7A100T-1CSG324C chip on Digilent Nexys 4 DDR FPGA development board. It uses only 2510 Flip Flops, 6054-bit Lookup Table, and 208 DSP48E of the FPGA chip. The system can achieve 93.87% accuracy with hardwired MLP-NN function. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
| Externí odkaz: |
|