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Wheat spike detection is crucial for estimating wheat yields and has a significant impact on the modernization of wheat cultivation and the advancement of precision agriculture. This study explores the application of the DETR (Detection Transformer) architecture in wheat spike detection, introducing a new perspective to this task. We propose a high-precision end-to-end network named WH-DETR, which is based on an enhanced RT-DETR architecture. Initially, we employ data augmentation techniques such as image rotation, scaling, and random occlusion on the GWHD2021 dataset to improve the model's generalization across various scenarios. A lightweight feature pyramid, GS-BiFPN, is implemented in the network's neck section to effectively extract the multi-scale features of wheat spikes in complex environments, such as those with occlusions, overlaps, and extreme lighting conditions. Additionally, the introduction of GSConv enhances the network precision while reducing the computational costs, thereby controlling the detection speed. Furthermore, the EIoU metric is integrated into the loss function, refined to better focus on partially occluded or overlapping spikes. The testing results on the dataset demonstrate that this method achieves an Average Precision (AP) of 95.7%, surpassing current state-of-the-art object detection methods in both precision and speed. These findings confirm that our approach more closely meets the practical requirements for wheat spike detection compared to existing methods. [ABSTRACT FROM AUTHOR]

This paper proposes a pre‐processing method for heart sound screening and extracts the high‐order spectral feature of phonocardiogram. Moreover, a multi‐convolutional neural network (mCNN) is constructed to achieve the classification of normal, aortic stenosis, mitral regurgitation, mitral stenosis, and mitral valve prolapse. First, the heart sound recordings are down‐sampled, denoised by wavelet transform, and normalized. Second, a new heart sound screening algorithm is proposed. The waveform of the heart sound recording is segmented and saved as an image which is performed by the gray‐scale processing to calculate the amplitude of the heart sound. The extremely noisy heart sound segments are screened out based on the amplitude information, and the remaining heart sound segments are spliced as pure heart sound recordings. After 50% superposition segmentation of the heart sound recordings, high‐order spectral features are extracted and image data are stored. Finally, a 34‐layer mCNN is specifically designed to boost the performance of heart sound classification through multi‐layer dimensionality reduction. Experimental results show that the proposed method has superior performance compared with the existing one. For the two‐category dataset, the accuracy with and without PCG screening is 97.99% and 99.42%, respectively. For the five‐category dataset, the average accuracy is 99%. [ABSTRACT FROM AUTHOR]

The identification of textile fiber materials is a tedious task. Traditional physical or chemical methods rely on specialist knowledge and expensive instruments. Currently, the deep learning-based methods are limited by the lack of sufficient image samples. In this paper, we propose a method for the classification of textile materials based on advanced densely connected convolutional networks (Densenet) using fabric surface images. Firstly, we selected a small sample dataset containing five types of materials and performed some pre-processing, including colour weakening and data enhancement. The lightweight network Densenet was then used as the main body to ensure the low parameters of the network, making it more suitable for the recognition of small sample image datasets. The network is then fused with the attention mechanism, Orthogonal Softmax Layer (OSL) and the depth-separable convolution mechanism, and we call the final network AONet. It has only 0.64 M (million) parameters. In this paper, we tested four metrics of AONet for five types of fabric images, and the results showed that the recognition accuracy was 95.61%. The precision, recall and F1 scores were 94.87%, 94.31% and 94.40%, respectively. [ABSTRACT FROM AUTHOR]

Abstract: The Ordovician Wufeng Formation and Silurian Longmaxi Formation are two of the most organic-rich and gas-prospective shale formations in the central Yangtze area, China. In this study, we investigate [...]