An Automatic Insect Recognition Algorithm in Complex Background Based on Convolution Neural Network
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.