Spatial and Efficient Channel Attention for Multi-Scale Smoke Detection

Efficient attention based deep fusion CNN for smoke detection in fog environment

Recently, the attention mechanism combining spatial and channel information has been widely used in various deep convolutional neural networks (CNNs), proving its great potential in improving model performance. However, this usually uses 2D global pooling operations to compress spatial information or scaling methods to reduce the computational overhead in channel attention. These methods will result in severe information loss. Therefore, we propose a Spatial channel attention mechanism that captures cross-dimensional interaction, which does not involve dimensionality reduction and brings significant performance improvement with negligible computational overhead. The proposed attention mechanism can be seamlessly integrated into any convolutional neural network since it is a lightweight general module. Our method achieves a performance improvement of 2.08% on ResNet and 1.02% on MobileNetV2 in top-one error rate on the ImageNet dataset.

Older People Issues and Innovations in Care 3rd edition

Accurate prediction of sea surface temperature (SST) is practically important to ocean-related fields. However, due to the nonlinear temporal characteristics and complex spatial correlation of SST, fully extracting its spatiotemporal features for dependable short-term SST prediction remains challenging. In this work, we presented a multilayer channel spatial attention convolutional LSTM (CSACL) network to better capture the spatiotemporal information of SST recordings. Using multiscale spatial attention to obtain global–local spatial information and dynamic channel attention for interchannel interaction, the CSA module achieves comprehensive extraction of spatial features in SST data. Moreover, the multilayer long short-term memory (LSTM) structure ensures that the network can capture the temporal dependency of short-term SST very well. Finally, we proposed a novel two-stage adaptive loss function training method to solve the accuracy degradation problem for longer prediction cycles. We have evaluated CSACL on three public datasets, and the results demonstrate its superior performance over other classical methods in short-term SST prediction.

Small Target Spillage Detection Algorithm Based on Spatial Attention and Channel Attention Mechanism

While deep learning-based methods have gained considerable improvements in remote sensing (RS) image change detection (CD), scale variations and pseudochanges hinder most supervised methods’ performance. The CD networks derived from other fields can be fronted with false alarms and miss detections in high-resolution RS images due to the weak feature representation ability. In this article, an attention-guided end-to-end change detection network (AGCDetNet) is proposed based on the fully convolutional network and attention mechanism. AGCDetNet learns to enhance the feature representation of change information and achieve accuracy improvements using spatial attention and channel attention. A spatial attention module (SPAM) promotes the discrimination between the changed objects and the background by adding the learned spatial attention to the deep features. Channelwise attention-guided interference filtering unit (CIFU)/atrous spatial pyramid pooling (CG-ASPP) module enhances the representation of multilevel features and multiscale context, respectively. Extensive experiments have been conducted on several public datasets for performance evaluation, including LEVIR-CD, WHU, Season-Varying, WV2, and ZY3. Experiment results demonstrate that AGCDetNet outperforms several state-of-the-art methods of accuracy and robustness. Specifically, AGCDetNet achieves the best F1-score on two datasets, i.e., LEVIR-CD (0.9076) and Season-Varying (0.9654).

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

Unmanned aerial vehicle insulator detection that aims to recognize defective insulators from transmission lines has made significant progress in recent years. However, it still faces challenges, such as the complex background of aerial images and the small memory of unmanned aerial vehicles. This paper proposes a refined insulator detection algorithm that integrates the attention mechanism in YOLOv8 to improve the feature extraction ability. Specifically, this paper introduces a fast vision transformers structure in the you only look once (YOLO) v8 backbone section to enhance feature extraction by capturing local and global features. Additionally, the global attention mechanism is incorporated in the neck for additional feature extraction by merging comprehensive spatial and channel information into the output. Furthermore, we amalgamate depth‐wise convolution, graph convolution, and residual operation in the global attention mechanism module. This design can mitigate the issues of gradient vanishing or exploding and meanwhile enhance the distinction between spatial attention and channel attention. The proposed model is then applied to a public dataset and a set of real images from a specific power station, and the detection results show that it outperforms many competitors in terms of accuracy, efficiency, and memory size.

Attention mechanisms are rapidly being noticed in the computer vision community. However, existing works focus on the design of a single attention module, and then employ the same attention module in all attention layers of an entire network, rendering sub-optimal performance. In this paper, we address a learning-to-attention problem by proposing Switchable Attention (SA), which learns to select different kinds of attention modules for variant blocks of a Deep Neural Network (DNN). SA employs three distinct scopes to compute the attention map, including local spatial attention (LSA), global spatial attention (GSA) and channel attention (CA). Especially, the introduced trainable parameters can be optimized with the network in an end-to-end manner. SA is a light-weight module which can be embedded in existing networks with little overhead of computation. We conduct several quantitative experiments, and SA boosts the performance of the baseline on various challenging benchmarks, such as CIFAR-100, ImageNet-1K, and MS COCO and different computer vision tasks, such as Image Classification and Object detection. Notably, SA outperforms the most attention methods with ResNet-50 on ImageNet-1K.

As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks.

Owing to its important role in video surveillance, person re-identification(re-ID) has become a hot research field of computer vision. The purpose of person re-ID is to identify the same person under different cameras. Domain adaptive person re-ID is to transfer the training model from labeled dataset to unlabeled dataset. Due to the neglect of key channels and key regions, the performance of most existing domain adaptive person re-ID methods is limited. To solve the above problem, this paper proposed a mutual attention learning network model (MALNet). It is based on channel attention and spatial attention mechanism. It consists of two channel-spatial attention convolutional neural networks (channel-spatial attention CNNs). Two networks are pre-trained in a source domain. They adapt to the target domain by the mutual attention learning strategy afterward. They distill and supervise each other to enhance the complementarity of MAL-Net. Moreover, a channel-spatial attention loss (CSAL) function is proposed to constrain and approximate the classification prediction distributions and feature triplet distributions. Extensive comparative experiments are carried out on two large-scale re-ID datasets (Market-1501 and DukeMTMC-reID). The performance of MAL-Net is better than other state-of-the-art methods, which proves the superiority and effectiveness of our method.

Despite the great success of attention mechanisms on object recognition, scene recognition remains a challenging problem. The reason is that discriminative regions are not evident in a scene image. For example, a tree in an image can be a cue to recognize a scene, but the tree cannot be the only cue for recognizing the scene. That means several scene categories (e.g. mountain, marsh, and river) can contain a tree. Thus sometimes, overall regions, rather than specific regions, need to be considered for scene recognition. To solve the problem, we propose Spatial-Channel Transformer (SC-Transformer). The SC-Transformer is a simple yet effective module that uses a new attention mechanism by incorporating the importance between the spatial and the channel domain for a given scene image. If the given scene image should be considered only within some specific regions, SC-Transformer turns off the channel attention, and vice versa. Furthermore, the attention mechanism used in our proposed method is advanced from previous approaches. Previous spatial and channel attention mechanisms were designed in a sequential or parallel manner. These mechanisms eventually combine spatial and channel attention together, so spatial and channel attention may often interfere with each other. In contrast to the previous works, we present a new mechanism that simultaneously considers spatial and channel attentions. We validate our approach on a large-scale scene recognition dataset and outperform the previous state-of-the-art spatial-channel attention mechanism. Experimental results demonstrate the efficacy of our attention mechanism for scene recognition.

Traditional flame and smoke detection mostly rely on temperature and smoke sensor, but the detection of temperature detector and smoke detector has a certain lag. In order to solve this problem of hysteresis and low accuracy, we propose an improved YOLOV3 algorithm combined with attention mechanism for flame and smoke detection. Firstly, a multi-scene large-scale flame and smoke image dataset is built. The localization and classification of the flame and smoke areas in the image are annotated precisely. The suspected areas of the flame and smoke in the image are obtained by color analysis, so that the suspected areas of the flame and smoke objects are concerned. Then combined with the feature extraction ability of deep network, the problem of flame and smoke detection is transformed into multi-classification and coordinate regression. Finally, the detection model of flame and smoke in multi-scene is obtained. Our experiments show the effectiveness of the improved YOLOv3 algorithm combined with attention mechanism in flame and smoke detection. Our proposed method achieves outstanding performance in the dataset of flame and smoke image. The detection speed also meets the need of real-time detection.KeywordsFlame and smoke detectionAttention mechanismsYOLOv3Deep learning

By recognizing students’ facial expressions in actual classroom situations, the students’ emotional states can be quickly uncovered, which can help teachers grasp the students’ learning rate, which allows teachers to adjust their teaching strategies and methods, thus improving the quality and effectiveness of classroom teaching. However, most previous facial expression recognition methods have problems such as missing key facial features and imbalanced class distributions in the dateset, resulting in low recognition accuracy. To address these challenges, this paper proposes LCANet, a model founded on a fused attention mechanism and a joint loss function, which allows the recognition of students’ emotions in real classroom scenarios. The model uses ConvNeXt V2 as the backbone network to optimize the global feature extraction capability of the model, and at the same time, it enables the model to pay closer attention to the key regions in facial expressions. We incorporate an improved Channel Spatial Attention (CSA) module as a way to extract more local feature information. Furthermore, to mitigate the class distribution imbalance problem in the facial expression dataset, we introduce a joint loss function. The experimental results show that our LCANet model has good recognition rates on both the public emotion datasets FERPlus, RAF-DB and AffectNet, with accuracies of 91.43%, 90.03% and 64.43%, respectively, with good robustness and generalizability. Additionally, we conducted experiments using the model in real classroom scenarios, detecting and accurately predicting students’ classroom emotions in real time, which provides an important reference for improving teaching in smart teaching scenarios.

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for clinical applications. Deep convolutional neural network methods such as DeepLabv3+ have successfully been applied for medical image segmentation, but multi-level features are seldom integrated seamlessly into different attention mechanisms, and few studies have fully explored the interactions between medical image segmentation and classification tasks. Herein, we propose a feature-compression-pyramid network (FCP-Net) guided by game-theoretic interactions with a hybrid loss function (HLF) for the medical image segmentation. The proposed approach consists of segmentation branch, classification branch and interaction branch. In the encoding stage, a new strategy is developed for the segmentation branch by applying three modules, e.g., embedded feature ensemble, dilated spatial mapping and channel attention (DSMCA), and branch layer fusion. These modules allow effective extraction of spatial information, efficient identification of spatial correlation among various features, and fully integration of multi-receptive field features from different branches. In the decoding stage, a DSMCA module and a multi-scale feature fusion module are used to establish multiple skip connections for enhancing fusion features. Classification and interaction branches are introduced to explore the potential benefits of the classification information task to the segmentation task. We further explore the interactions of segmentation and classification branches from a game theoretic view, and design an HLF. Based on this HLF, the segmentation, classification and interaction branches can collaboratively learn and teach each other throughout the training process, thus applying the conjoint information between the segmentation and classification tasks and improving the generalization performance. The proposed model has been evaluated using several datasets, including ISIC2017, ISIC2018, REFUGE, Kvasir-SEG, BUSI, and PH2, and the results prove its competitiveness compared with other state-of-the-art techniques.