An Image Recognition Model Based on Improved Convolutional Neural Network

Abstract In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

Download Full-text

Image Recognition of Male Oilseed Rape (Brassica napus) Plants Based on Convolutional Neural Network for UAAS Navigation Applications on Supplementary Pollination and Aerial Spraying

Agriculture ◽

10.3390/agriculture12010062 ◽

2022 ◽

Vol 12 (1) ◽

pp. 62

Author(s):

Zhu Sun ◽

Xiangyu Guo ◽

Yang Xu ◽

Songchao Zhang ◽

Xiaohui Cheng ◽

...

Keyword(s):

Neural Network ◽

Brassica Napus ◽

Convolutional Neural Network ◽

Seed Production ◽

Image Recognition ◽

Oilseed Rape ◽

Center Line ◽

Recognition Model ◽

Aerial Spraying ◽

Mean Square Errors

To ensure the hybrid oilseed rape (OSR, Brassica napus) seed production, two important things are necessary, the stamen sterility on the female OSR plants and the effective pollen spread onto the pistil from the OSR male plants to the OSR female plants. The unmanned agricultural aerial system (UAAS) has developed rapidly in China. It has been used on supplementary pollination and aerial spraying during the hybrid OSR seed production. This study developed a new method to rapidly recognize the male OSR plants and extract the row center line for supporting the UAAS navigation. A male OSR plant recognition model was constructed based on the convolutional neural network (CNN). The sequence images of male OSR plants were extracted, the feature regions and points were obtained from the images through morphological and boundary process methods and horizontal segmentation, respectively. The male OSR plant image recognition accuracies of different CNN structures and segmentation sizes were discussed. The male OSR plant row center lines were fitted using the least-squares method (LSM) and Hough transform. The results showed that the segmentation algorithm could segment the male OSR plants from the complex background. The highest average recognition accuracy was 93.54%, and the minimum loss function value was 0.2059 with three convolutional layers, one fully connected layer, and a segmentation size of 40 pix × 40 pix. The LSM is better for center line fitting. The average recognition model accuracies of original input images were 98% and 94%, and the average root mean square errors (RMSE) of angle were 3.22° and 1.36° under cloudy day and sunny day lighting conditions, respectively. The results demonstrate the potential of using digital imaging technology to recognize the male OSR plant row for UAAS visual navigation on the applications of hybrid OSR supplementary pollination and aerial spraying, which would be a meaningful supplement in precision agriculture.

Download Full-text