Research on Insulator Defect Detection Model Based on Improved YOLOv8

In recent years, attention mechanisms have shown great potential in various computer vision tasks. However, most existing methods focus on developing more complex attention modules for better performance, which inevitably increases the complexity of the model. To overcome performance and complexity tradeoffs, this paper proposes efficient spatial and channel attention (ESCA), a symmetric, comprehensive, and efficient attention module. By analyzing squeeze-and-excitation (SE), convolutional block attention module (CBAM), coordinate attention (CA), and efficient channel attention (ECA) modules, we abandon the dimension-reduction operation of SE module, verify the negative impact of global max pooling (GMP) on the model, and apply a local cross-channel interaction strategy without dimension reduction to learn attention. We not only care about the channel features of the image, we also care about the spatial location of the target on the image, and we take into account the effectiveness of channel attention, so we designed the symmetric ESCA module. The ESCA module is effective, as demonstrated by its application in the ResNet-50 classification benchmark. With 26.26 M parameters and 8.545 G FLOPs, it introduces a mere 0.14% increment in FLOPs while achieving over 6.33% improvement in Top-1 accuracy and exceeding 3.25% gain in Top-5 accuracy. We perform image classification and object detection tasks on ResNet, MobileNet, YOLO, and other architectures on popular datasets such as Mini ImageNet, CIFAR-10, and VOC 2007. Experiments show that ESCA can achieve great improvement in model accuracy at a very small cost, and it performs well among similar models.

In this paper, we focus on the semantic image synthesis task that aims at transferring semantic label maps to photo-realistic images. Existing methods lack effective semantic constraints to preserve the semantic information and ignore the structural correlations in both spatial and channel dimensions, leading to unsatisfactory blurry and artifact-prone results. To address these limitations, we propose a novel Dual Attention GAN (DAGAN) to synthesize photo-realistic and semantically-consistent images with fine details from the input layouts without imposing extra training overhead or modifying the network architectures of existing methods. We also propose two novel modules, i.e., position-wise Spatial Attention Module (SAM) and scale-wise Channel Attention Module (CAM), to capture semantic structure attention in spatial and channel dimensions, respectively. Specifically, SAM selectively correlates the pixels at each position by a spatial attention map, leading to pixels with the same semantic label being related to each other regardless of their spatial distances. Meanwhile, CAM selectively emphasizes the scale-wise features at each channel by a channel attention map, which integrates associated features among all channel maps regardless of their scales. We finally sum the outputs of SAM and CAM to further improve feature representation. Extensive experiments on four challenging datasets show that DAGAN achieves remarkably better results than state-of-the-art methods, while using fewer model parameters.

Case instance segmentation of small farmland based on Mask R-CNN of feature pyramid network with double attention mechanism in high resolution satellite images

The refined segmentation of nuclei and the cytoplasm is the most challenging task in the automation of cervical cell screening. The U-Shape network structure has demonstrated great superiority in the field of biomedical imaging. However, the classical U-Net network cannot effectively utilize mixed domain information and contextual information, and fails to achieve satisfactory results in this task. To address the above problems, a module based on global dependency and local attention (GDLA) for contextual information modeling and features refinement, is proposed in this study. It consists of three components computed in parallel, which are the global dependency module, the spatial attention module, and the channel attention module. The global dependency module models global contextual information to capture a priori knowledge of cervical cells, such as the positional dependence of the nuclei and cytoplasm, and the closure and uniqueness of the nuclei. The spatial attention module combines contextual information to extract cell boundary information and refine target boundaries. The channel and spatial attention modules are used to provide adaption of the input information, and make it easy to identify subtle but dominant differences of similar objects. Comparative and ablation experiments are conducted on the Herlev dataset, and the experimental results demonstrate the effectiveness of the proposed method, which surpasses the most popular existing channel attention, hybrid attention, and context networks in terms of the nuclei and cytoplasm segmentation metrics, achieving better segmentation performance than most previous advanced methods.

In recent years, the research of deep learning has received extensive attention, and many breakthroughs have been made in various fields. On this basis, a neural network with the attention mechanism has become a research hotspot. In this paper, we try to solve the image classification task by implementing channel and spatial attention mechanism which improve the expression ability of neural network model. Different from previous studies, we propose an attention module consisting of channel attention module (CAM) and spatial attention module (SAM). The proposed module derives attention graphs from channel dimension and spatial dimension respectively, then the input features are selectively learned according to the importance of the features. Besides, this module is lightweight and can be easily integrated into image classification algorithms. In the experiment, we combine the deep residual network model with the attention module and the experimental results show that the proposed method brings higher image classification accuracy. The channel attention module adds weight to the signals on different convolution channels to represent the correlation. For different channels, the higher the weight, the higher the correlation which required more attention. The main function of spatial attention is to capture the most informative part in the local feature graph, which is a supplement to channel attention. We evaluate our proposed module based on the ImageNet-1K and Cifar-100 respectively. Through a large number of comparative experiments, our proposed model achieved outstanding performance.

Impurity monitoring study for corn kernel harvesting based on machine vision and CPU-Net

Sheep behavior recognition helps to monitor the health status of sheep and prevent the outbreak of infectious diseases. Aiming at the problems of low detection accuracy and slow speed due to the crowding of sheep in real farming scenarios, which can easily obscure each other, this study proposes a lightweight sheep behavior recognition model based on the YOLOv8n model. First, the Convolutional Block Attention Module (CBAM) is introduced and improved in the YOLOv8n model, and the channel attention module and spatial attention module are changed from serial to parallel to construct a novel attention mechanism, PCBAM, to enhance the network’s attention to the sheep and eliminate redundant background information; second, the ordinary convolution in the backbone network is replaced with depth-separable convolution, which effectively reduces the number of parameters in the model and reduces the computational complexity. The study takes the housed breeding sheep as the test object, installs a camera diagonally above the sheep pen to collect images and makes a data set for testing, and in order to verify the superiority of the PD-YOLO model, compares it with a variety of target detection models. The experimental results show that the mean average precision (mAP) of the model proposed in this paper are 95.8%, 98.9%, and 96.2% for the three postures of sheep lying, feeding, and standing, respectively, which are 8.5%, 0.8%, and 0.8% higher than those of YOLOv8n, respectively, and the size of the model has been reduced by 13.3% and the amount of computation has been reduced by 12.1%. The inference speed reaches 52.1 FPS per second, which is better than other models in meeting the real-time detection requirement. To verify the practicality of this research method, the PD-YOLO model was deployed on the RK3399Pro development board for testing, and a high inference speed was achieved. It can provide effective technical support for sheep smart farming.

In recent years, vein biometrics has gained significant attention due to its high security and privacy features. While deep neural networks have become the predominant classification approaches for their ability to automatically extract discriminative vein features, they still face certain drawbacks: 1) Existing transformer-based vein classifiers struggle to capture interactive information among different attention modules, limiting their feature representation capacity; 2) Current label enhancement methods, although effective in learning label distributions for classifier training, fail to model long-range relations between classes. To address these issues, we present ALE-IVT, an Attention Label Enhancement-based Interactive Vein Transformer for palm-vein recognition. First, to extract vein features, we propose an interactive vein transformer (IVT) consisting of three branches, namely spatial attention, channel attention, and convolutional module. In order to enhance performance, we integrate an interactive module that facilitates the sharing of discriminative features among the three branches. Second, we explore an attention-based label enhancement (ALE) approach to learn label distribution. ALE employs a self-attention mechanism to capture correlation between classes, enabling the inference of label distribution for classifier training. As self-attention can model long-range dependencies between classes, the resulting label distribution provides enhanced supervised information for training the vein classifier. Finally, we combine ALE with IVT to create ALE-IVT, trained in an end-to-end manner to boost the recognition accuracy of the IVT classifier. Our experiments on three public datasets demonstrate that our IVT model surpasses existing state-of-the-art vein classifiers. In addition, ALE outperforms current label enhancement approaches in term of recognition accuracy.

Spherical image acquisition and defect detection of nuclear fuel based on the line scan

In this paper, we propose a novel fusion framework based on a dense network with channel and spatial attention (CSpA-DN) for PET and MR images. In our approach, an encoder composed of the densely connected neural network is constructed to extract features from source images, and a decoder network is leveraged to yield the fused image from these features. Simultaneously, a self-attention mechanism is introduced in the encoder and decoder to further integrate local features along with their global dependencies adaptively. The extracted feature of each spatial position is synthesized by a weighted summation of those features at the same row and column with this position via a spatial attention module. Meanwhile, the interdependent relationship of all feature maps is integrated by a channel attention module. The summation of the outputs of these two attention modules is fed into the decoder and the fused image is generated. Experimental results illustrate the superiorities of our proposed CSpA-DN model compared with state-of-the-art methods in PET and MR images fusion according to both visual perception and objective assessment.

RGAM: A novel network architecture for 3D point cloud semantic segmentation in indoor scenes

The development of high-resolution remote sensing and deep learning techniques has significantly advanced the extraction of urban structures. However, densely built areas pose challenges due to the complex geometry and spatial distribution of structures, resulting in uncertainties in their frequency-domain characterization. To resolve these issues, this research proposes WaveFuseNet, an effective network designed for multiscale building extraction sensitive to frequency information. Within its encoder, WaveFuseNet incorporates a Multiscale Wavelet Transform Convolutional (MWTC) module to extract both high-frequency and low-frequency features across scales. Furthermore, the Adaptive Spatial and Channel Attention (ASCA) module is integrated to enhance global context modeling by strengthening the synergy between spatial and channel attention. The decoder employs an Adaptive Feature Fusion Decoder that performs learnable, weighted fusion of multiscale features and upsampling operations to preserve spatial-frequency characteristics. Comprehensive evaluations conducted on the WHU dataset, the Dataset of Building Instances of Typical Cities in China, and the Massachusetts Buildings Dataset demonstrate the model’s excellent performance, reaching IoU values of 91.15%, 75.76%, and 72.56%, respectively, with only 16.13 M parameters. These results demonstrate that WaveFuseNet is an effective and efficient scheme for extracting urban buildings at large scales, providing valuable support for smart city development and sustainable urban planning. Access to the source code is provided through https://github.com/786942053/WavefuseNet/blob/main/README.md.

After decades of extensive research, the field of 2D face recognition is fruitful. However, 2D face recognition is sensitive to changes in pose, facial expression and illumination. 3D face recognition has great potential due to the inherent invariance of pose and illumination changes, but these 3D face images are still difficult to acquire due to several issues such as cost and accessibility. In contrast, RGB-D images captured by low-cost depth sensors such as Kinect are relatively easy to acquire, and research on such low-quality inputs is still limited. In this thesis, an end-to-end multimodal fusion method based on spatial and channel attention is proposed to effectively fuse two image modalities, RGB and depth, to enhance RGB-D face recognition. Our work is specified as follows: firstly, three branches combined with the spatial and channel attention modules are established on the basis of ResNet18, so that the features of three modalities (RGB, depth map, and their fusion modalities, respectively) are obtained. The features under these three modalities are then fused, fed into a shared layer, and the fused features are further learned for deeper discriminative features and passed through a spatial attention vectorization module at the end. The method achieves good results on the Intellifusion RGBD dataset, while we perform ablation trials, which well reveal the contribution of each component of our method to the final performance.

Convolution neural networks (CNNs) have been achieving increasing attention for the artificial bandwidth extension (ABE) task recently. However, these methods use the flipped low-frequency phase to reconstruct speech signals, which may lead to the well-known invalid short-time Fourier Transform (STFT) problem. The convolutional operations only enable networks to construct informative features by fusing both channel-wise and spatial information within local receptive fields at each layer. In this paper, we introduce a Time-Frequency Network (TFNet) with channel attention (CA) and non-local (NL) modules for ABE. The TFNet exploits the information from both time and frequency domain branches concurrently to avoid the invalid STFT problem. To capture the channels and space dependencies, we incorporate the CA and NL modules to construct a proposed fully convolutional neural network for the time and frequency branches of TFNet. Experimental results demonstrate that the proposed method outperforms the competing method.

Object tracking can be considered as an object detection task. However, in general, an object detector cannot differentiate among intraclass instances and cannot fully exploit the spatiotemporal clues between successive frames. In order to solve these problems, in this paper, we proposed a novel object tracking framework based on object detection. We name the new framework TSTrack. Firstly, transformer is used in the target guidance module to extract the object’s features to guide the backbone network to complete the positioning task, which can solve the problem of distinguishing the instances within the classification. Secondly, self-attention mechanism is introduced into the model, which is mainly completed by spatial attention module (SAM) and channel attention module (CAM). By using the characteristics of self-attention mechanism, the attention matrix is dynamically self-learned in two dimensions of space and channel to obtain a more robust object representation. The proposed model is evaluated on the OTB-2013, OTB-50, OTB-2015 and VOT-2016 benchmarks, and the experimental results demonstrate the excellent tracking performance and robustness of our model.