In agriculture, pest always causes the major damage in fields and results in significant crop yield losses. Currently, manual pest classification and counting are very time-consuming and many subjective factors can affect the population counting accuracy. In addition, the existing pest localization and recognition methods based on Convolutional Neural Network (CNN) are not satisfactory for practical pest prevention in fields because of pests' different scales and attitudes. In order to address these problems, an effective data augmentation strategy for CNN-based method is proposed in this paper. In training phase, we adopt data augmentation through rotating images by various degrees followed by cropping into different grids. In this way, we could obtain a large number of extra multi-scale examples that could be adopted to train a multi-scale pest detection model. In terms of test phase, we utilize the test time augmentation (TTA) strategy that separately inferences input images with various resolutions using the trained multi-scale model. Finally, we fuse these detection results from different image scales by non-maximum suppression (NMS) for the final result. Experimental results on wheat sawfly, wheat aphid, wheat mite and rice planthopper in our domain specific dataset, show that our proposed data augmentation strategy achieves the pest detection performance of 81.4% mean Average Precision (mAP), which improves 11.63%, 7.93% ,4.73% compared to three stateof-the-art approaches.
The effective segmentation and 3-D rendering of the esophagus and esophageal cancer from the computed tomography (CT) images can assist doctors in diagnosing esophageal cancer. Irregular and vague boundary causes great difficulty in the segmentation of esophagus and esophageal cancer. In this paper, U-Net Plus is proposed to segment esophagus and esophageal cancer from a 2-D CT slice. In the new network architecture, two blocks are employed to enhance the feature extraction performance of complex abstract information, which can effectively resolve irregular and vague boundaries. A block is a skip connection operation that is similar to convolution. The architecture is trained through a dataset of 1924 slices from 10 CT scans and tested through 295 slices from 6 CT scans. The training and test datasets are expanded tenfold to simulate the segmentation of the 3-D CT image. Using the new framework, we report a 0.79 ± 0.20 dice value and 5.87 ± 9.91 Hausdorff distance. A semi-automatic scheme is then designed for the 3-D segmentation of the esophagus or esophageal cancer. The 3-D rendering of the esophagus or esophageal cancer is implemented to assist in the diagnosis of esophageal cancer.INDEX TERMS Esophageal cancer, image segmentation, deep learning, computed tomography (CT).
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