Recent works have shown that deep‐learning‐derived methods based on convolutional neural network can achieve high performance in terms of accuracy when applied to computer vision task such as object detection, segmentation and classification particularly on hyperspectral image. However, the existing methods have long training times. To reduce the training time and increase the accuracy, this paper proposed a new 3D2D convolutional neural network combined for hyperspectral image classification. For this purpose, a 3D fast learning block (depthwise separable convolution block and a fast convolution block) followed by a 2D convolutional neural network was introduced to extract spectralspatial features. Four datasets were used for the experiment purpose and the results showed that the proposed method achieved excellent result on both small and large training data compared with existing methods. The proposed method increased the overall accuracies by 2% on UP and KSC datasets while significantly reducing the training time on IP and KSC datasets, respectively. The proposed method increased all accuracies for at least 6% on IP, KSC and UP datasets when compared to some state‐of‐the‐art methods. Also, it reduced considerably the training and testing time on IP and KSC datasets when fast convolution block alone is involved.

Soybean plays an important role in food, medicine, and industry. The quality inspection of soybean is essential for soybean yield and the agricultural economy. However, soybean pest is an important factor that seriously affects soybean yield, among which leguminivora glycinivorella matsumura is the most frequent pest. Aiming at the problem that the traditional detection methods have low accuracy and need a large number of samples to train the model, this paper proposed a detection method for leguminivora glycinivorella matsumura based on an A-ResNet (Attention-ResNet) meta-learning model. In this model, the ResNet network was combined with Attention to obtain the feature vectors that can better express the samples, so as to improve the performance of the model. As well, the classifier was designed as a multi-class support vector machine (SVM) to reduce over-fitting. Furthermore, in order to improve the training stability of the model and the prediction performance on the testing set, the traditional Batch Normalization was replaced by the Layer Normalization, and the Label Smooth method was used to punish the original loss. The experimental results showed that the accuracy of the A-ResNet meta-learning model reached 94.57 ± 0.19%, which can realize rapid and accurate nondestructive detection, and provides theoretical support for the intelligent detection of soybean pests.

Lung images often have the characteristics of strong noise, uneven grayscale distribution, and complex pathological structures, which makes lung image segmentation a challenging task. To solve this problems, this paper proposes an initial lung mask extraction algorithm that combines threshold and gradient. The gradient used in the algorithm is obtained by the time series feature extraction method based on differential memory (TFDM), which is obtained by the grayscale threshold and image grayscale features. At the same time, we also proposed a lung contour repair algorithm based on the improved convex hull method to solve the contour loss caused by solid nodules and other lesions. Experimental results show that on the COVID-19 CT segmentation dataset, the advanced lung segmentation algorithm proposed in this article achieves better segmentation results and greatly improves the consistency and accuracy of lung segmentation. Our method can obtain more lung information, resulting in ideal segmentation effects with improved accuracy and robustness.

In order to efficiently retrieve the same or similar fabric samples, a fabric image retrieval model based on deep hashing was proposed. The model was improved on the basis of MobileNetV1, and the performance of the model was improved by combining the h-swish activation function and attention mechanism module. The hashing method was used to solve the problem of low feature matching speed caused by high-dimensional feature output. By combining the label information and similarity information of the images, a new loss function was constructed, which solved the problem of low model accuracy caused by the large feature difference between similar samples and the small feature difference between heterogeneous samples in fabric images. The experimental results on self-built fabric image dataset showed that the feature extraction time of the proposed algorithm was 0.25 ms, and the MAP reached 93.2\(\%\), which can take into account the fabric retrieval speed and improve retrieval accuracy at the same time. As a result, it has certain application prospects.