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Rapid and automatic plant image identification method based on deep learning YOLO model

An automatic recognition and deep learning technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve problems affecting the classification and recognition of target objects, loss of target image feature information, etc., to achieve fast and accurate automatic recognition, The effect of improving generalization ability, speeding up matching speed and training speed

Pending Publication Date: 2022-04-26
HENNAN ELECTRIC POWER SURVEY & DESIGN INST +1
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Problems solved by technology

However, due to some objective factors, the collected images will be affected by factors such as lighting, shooting angle, occlusion, etc., resulting in the loss of target image feature information, which will affect the classification and recognition of target objects

Method used

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  • Rapid and automatic plant image identification method based on deep learning YOLO model
  • Rapid and automatic plant image identification method based on deep learning YOLO model
  • Rapid and automatic plant image identification method based on deep learning YOLO model

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Embodiment Construction

[0019] Such as figure 1 As shown, first normalize the input image, make sure that the processed image is consistent with the size required by the model, and then divide it into grids to form S×S grids. If the center of the target object coincides with the grid , the target needs to be identified at this time. Compare the specified threshold with the recognition result, and finally output the final target prediction value.

[0020] a. Add deformable convolution to the YOLOV3 model, so that the shape of the convolution kernel adapts to the contour of the target, which can achieve the standard of refined network extraction features; in step a, deformable convolution refers to the use of the anchor box idea. The width and height are determined by clustering, small objects and large objects are predicted separately, and the anchorbox evaluation is assigned and processed in the three-scale feature output layer.

[0021] b. Through the combination of the two processes of forward pr...

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Abstract

The invention discloses a plant image rapid automatic identification method based on a deep learning YOLO model, and the method comprises the following steps: a, adding deformable convolution in a YOLOV3 model, enabling the shape of a convolution kernel to adapt to the contour of a target, and achieving the standard of refined network extraction features; b, through combination of two processes of forward propagation and back propagation, parameters are optimized and adjusted, and expected output and extracted feature output are kept consistent; c, in order to accelerate model search matching speed and training speed, inter-layer node data is transmitted based on a GPU, and networks in the same layer are processed in parallel to obtain ideal response speed; and d, realizing the rapid and automatic plant image identification method by using the results of a and b and based on the operation strategy of c. According to the method, the deformable convolution model of YOLOV3 is used, model training is carried out through deep learning, rapid and automatic plant image recognition is realized, and the speed and accuracy of image recognition are effectively improved.

Description

technical field [0001] The invention belongs to the field of fast automatic identification of plant images. Specifically, it is based on the deep learning YOLO model to conduct rapid automatic identification algorithm research on plant images. The main application is to quickly, automatically and accurately identify various types of plants in plant image collections. Background technique [0002] Plants are a very wide range of life forms on the earth, which are directly related to the living environment of human beings. At present, plant identification mainly relies on the practical experience of practitioners in related industries and experienced experts, with heavy workload and low efficiency. In recent years, with the rapid development of social science and technology and economy, computer hardware has been further updated, and its performance has been improved day by day. Digital image acquisition equipment has been widely used, and the storage space of equipment has co...

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Application Information

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IPC IPC(8): G06V20/10G06K9/62G06N3/04G06N3/08G06V10/774G06V10/82
CPCG06N3/08G06N3/045G06F18/214
Inventor 孙步阳高首都剧成宇张俊鹏王胜磊蒋硕颜李珂吕献林
Owner HENNAN ELECTRIC POWER SURVEY & DESIGN INST
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