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Crop pest detection method based on F-SSD-IV3

A technology of F-SSD-IV3 and detection methods, applied in the field of deep learning and computer vision, can solve the problems of small target objects, unbalanced detection speed and detection accuracy, and small number of pest image samples, so as to improve detection The effect of performance and

Active Publication Date: 2019-09-10
ZHEJIANG UNIV
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Problems solved by technology

[0005] In order to solve the problem that the existing target detection algorithm cannot balance the contradiction between detection speed and detection accuracy well, and at the same time aiming at the characteristics of the existing pest image samples such as small number of samples, small target objects, various posture changes and easy occlusion, the present invention Improved the SSD target detection algorithm and proposed a new target detection method F-SSD-IV3 for detecting crop pests

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Embodiment Construction

[0036] The present invention will be described in detail below in conjunction with the embodiments and accompanying drawings, but the present invention is not limited thereto.

[0037] (1) Experimental data: The present invention adopts the data set of typical field crop pests collected by the Agricultural Information Technology Research Institute of Zhejiang University. The pest images in the data set cover different image sizes, light conditions, occlusion degrees, shooting angles, target pest sizes, etc. information. The images in the database are randomly and evenly distributed in the training set, validation set and test set with a ratio of 7:2:1. The data trains the model on the training set, evaluates on the validation set to select model parameters, and finally uses the test set to check the model performance and efficiency.

[0038] (2) Experimental environment: Specifications of the experimental workstation: memory 32GB, operating system is Linux Ubuntu 16.04, CPU i...

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Abstract

The invention discloses a method based on F-. SSD The crop pest detection method of the IV3 comprises the following steps that (1) pest images are collected, and a crop pest database is constructed;(2) constructing F; SSD According to the IV3 target detection algorithm, Inception V3 is used for replacing VGG-; 16 is used as a feature extractor, a feature fusion method is designed for output feature maps of different scales to fuse context information, and finally a Sofer NMS is used for fine tuning of candidate boxes. And (3) during training, the network is optimized, and the detection performance and the generalization capability of the model are improved by using a method of data amplification and Dropout layer addition.

Description

technical field [0001] The invention belongs to the field of deep learning and computer vision, and in particular relates to a method for detecting crop pests based on F-SSD-IV3. Background technique [0002] With the continuous growth of the global population, the demand for food is also increasing significantly. Due to the influence of the natural environment and the factors of the crop itself, crops will inevitably be attacked by pests in different growth stages. If the pests cannot be detected and eliminated in time, it may cause an outbreak of pests. The outbreak of large-scale pests will affect the healthy growth of crops, thereby causing greater damage to the yield and quality of crops. [0003] Traditional pest identification is based on morphological characteristics such as shape, color, and texture, and relies on manual identification methods. The results are subjectivity / poor timeliness / labor-intensive. Early identification of pests is based on template matching t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/58G06F16/51G06F16/55G06N3/04G06V20/00
CPCG06F16/5866G06F16/51G06F16/55G06N3/047G06N3/045G06V20/188G06V20/00G06V10/454G06V10/82G06F18/2413G06F18/253
Inventor 何勇吴剑坚曾鸿许剑
Owner ZHEJIANG UNIV
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