Spore detection algorithm of wheat powdery mildew based on weight adaptive feature fusion

Hao Niu; Botao Wang

doi:10.1117/12.2639187

15 July 2022 Spore detection algorithm of wheat powdery mildew based on weight adaptive feature fusion

Hao Niu, Botao Wang

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 1225814 (2022) https://doi.org/10.1117/12.2639187
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

Aiming at the characteristics of small targets, many interferents and inconspicuous features of spore images of wheat powdery mildew, a weight adaptive feature fusion model is proposed based on SSD network structure to improve the accuracy of spore detection. Firstly, a feature fusion path is constructed to recursively fuse features of various scales from deep to shallow, and at the same time, a layer of feature matrix is added to enhance the utilization of deep and shallow features by the network; Secondly, a hybrid attention module is proposed, which redistributes the weights of features adaptively to enhance the ability of extracting network context information. Finally, the k-means algorithm is used to set the shape of the prior box, which effectively improves the problem that it is difficult to manually adjust the hyperparameter of the neural network. The AP of powdery mildew spores was 91.17%, Compared with the classical SSD detection method, it has been greatly improved.

Citation Download Citation

Hao Niu and Botao Wang "Spore detection algorithm of wheat powdery mildew based on weight adaptive feature fusion", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 1225814 (15 July 2022); https://doi.org/10.1117/12.2639187

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KEYWORDS

Detection and tracking algorithms

Target detection

Feature extraction

Image fusion

Network architectures

Neural networks

Convolution

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