Research On Color Sorting Image Processing Algorithm For Cranular Crops

Color selection equipment is an application of machine vision in the field of industrial and agricultural sorting.At present,the color sorting system usually adopts a DSP+FPGA embedded hardware platform,and high-resolution images are used to classify by setting a threshold.The platform needs enough storage space,and there are disadvantages that the classification accuracy depends on experience.In recent years,the development of GPUs has provided the hardware basis for the application of complex image processing algorithms in color-selection systems.This paper combines the DSP+FPGA platform and the CPU+GPU platform to study the image compression algorithm,image classification algorithm and feature parameter detection algorithm selected by color.Firstly,a color-selection image processing hardware platform is set up to complete image acquisition,display,and processing.In the DSP+FPGA platform,a high-resolution color linear array CCD camera is used to acquire images.In the FPGA,a dual-port RAM is built to realize the data storage of the Camera Link interface.The VGA is used to control the image display,and the data is transmitted with the DSP.In the CPU+GPU platform,the low-resolution industrial camera and the image capture card are used to complete image acquisition and a Tensor Flow deep learning framework is built.Secondly,the image compression and reduction algorithm in color sorting are investigated.By selecting the wavelet function through comparison and analysis,the wavelet transform image decomposition and reconstruction are realized.Simulation analysis of EZW and SPIHT encoding methods,the results show that the large amount of computation,it is difficult to ensure real-time.This paper combines the image features of granular crops with color sorting,and proposes a simplified wavelet transform image compression algorithm.It directly performs Huffman coding on the low frequency coefficients after wavelet decomposition.The algorithm is transplanted to DSP for testing.The results show that the algorithm has higher compression ratio and real-time performance under the condition of satisfying the image quality.The color classification image classification algorithm based on deep learning is studied.Peanut image data sets are first established,which are divided into training set and test set.Convolutional neural network algorithm in deep learning is used to implement image classification.In order to improve the classification accuracy and real-time performance,this paper optimizes the data set according to the classification results;it optimizes the convolutional neural network from several aspects such as reducing over-fitting,accelerating the training convergence speed,and simplifying the network structure.The test results show that the optimization program has achieved the expected goal.Finally,the feature parameter detection algorithm is studied.For low-resolution sample images,two super-resolution reconstruction methods based on deep learning are researched,including SRCNN and ESPCN.According to image reconstruction quality and running time,ESPCN is selected for super-resolution reconstruction.The peanut image is then subjected to color processing,filtering and morphological operations,followed by edge detection and damage detection.Finally,feature parameter detection algorithm is written in C language and Python language,respectively,and transplanted to DSP + FPGA platform and CPU + GPU platform test.The results show that the algorithm can accurately complete the detection of characteristic parameters of peanut edges and damaged areas in real time.