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- Title
An Automatic Insect Recognition Algorithm in Complex Background Based on Convolution Neural Network.
- Authors
Xianrong Zhang; Gang Chen
- Abstract
The existing insect recognition methods mostly segment the target region by traditional classification technology, failing to achieve a high accuracy in complex background. To solve the problem, this paper introduces the morphology-based edgeless active contour strategy to segment insects in complex background. The strategy integrates the morphological operation of gray image, and detects insect contours by narrow-band fast method. To enhance the background diversity of new samples, the authors improved the synthetic minority over-sampling technique (SMOTE) algorithm into a variable weight edge enhancement algorithm. Based on the SMOTE algorithm, the proposed algorithm increases the weight of the edge area as adjacent images are superimposed into a new image, making the background of the new image more complex. Finally, the proposed method was coupled with DenseNet-121 to recognize insects in images with complex background. The results show that the accuracy of the network was nearly 10% higher on the balanced set than on the unbalanced set, suggesting that our method is feasible and accurate.
- Publication
Traitement du Signal, 2020, Vol 37, Issue 5, p793
- ISSN
0765-0019
- Publication type
Academic Journal
- DOI
10.18280/ts.370511