Insect Detection and Classification Based on an Improved Convolutional Neural Network
| Autor: | Jun Zhang, Chengjun Xie, DeNan Xia, Bing Wang, Peng Chen |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Process (engineering) convolutional neural network 02 engineering and technology Machine learning computer.software_genre lcsh:Chemical technology 01 natural sciences Biochemistry Convolutional neural network Field (computer science) Article Analytical Chemistry 0202 electrical engineering electronic engineering information engineering lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Artificial neural network business.industry 010401 analytical chemistry Atomic and Molecular Physics and Optics Object detection 0104 chemical sciences Statistical classification field crops region proposal network insect detection 020201 artificial intelligence & image processing Artificial intelligence business computer VGG19 |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 18, Iss 12, p 4169 (2018) Sensors Volume 18 Issue 12 |
| ISSN: | 1424-8220 |
| Popis: | Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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