To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease rec...To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.展开更多
Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior perfo...Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.展开更多
Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image.Local features extracted by convolutions,etc.,capture finegrained details such as edges and te...Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image.Local features extracted by convolutions,etc.,capture finegrained details such as edges and textures,while global features extracted by full connection layers,etc.,represent the overall structure and long-range relationships within the image.These features are crucial for accurate object detection,yet most existing methods focus on aggregating local and global features,often overlooking the importance of medium-range dependencies.To address this gap,we propose a novel full perception module(FPModule),a simple yet effective feature extraction module designed to simultaneously capture local details,medium-range dependencies,and long-range dependencies.Building on this,we construct a full perception head(FP-Head)by cascading multiple FP-Modules,enabling the prediction layer to leverage the most informative features.Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization,achieving 2.7−5.7 APval gains when integrated into standard object detectors.Notably,the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance.The code will be released at https://github.com/Idcogroup/FP-Head.展开更多
针对复杂海洋环境中存在背景噪声、海洋垃圾特征模糊和目标尺度小的检测挑战,本文提出一种基于改进CenterNet的海洋垃圾无锚检测算法——MG-CenterNet。引入GB(green-blue)注意力机制,通过关注海洋图像绿色、蓝色通道来增强特征提取;利...针对复杂海洋环境中存在背景噪声、海洋垃圾特征模糊和目标尺度小的检测挑战,本文提出一种基于改进CenterNet的海洋垃圾无锚检测算法——MG-CenterNet。引入GB(green-blue)注意力机制,通过关注海洋图像绿色、蓝色通道来增强特征提取;利用跨层特征聚合(cross-layer feature aggregation,CFA)模块丰富关键特征反馈,使模型获取更多像素级语义信息从而精准分类图像;构造完全交并比(complete intersection over union,CIoU)损失函数优化边界框匹配度,进一步提高目标定位精度。MG-CenterNet在TrashCan数据集和自建数据集上分别取得了77.98%和76.92%的平均精确率均值(mean average precision,m AP),推理速度分别达到27.18帧/s和26.98帧/s。研究结果证明MG-CenterNet在检测精度上显著优于其他算法,满足实时检测的要求。低对比度及遮挡条件下的验证实验进一步证明了所提出算法的鲁棒性和可靠性,为复杂环境中的海洋垃圾检测提供了科学参考。展开更多
To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning...To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.展开更多
基金funded by the Science and Technology Development Program of Jilin Province(20190301024NY)the Precision Agriculture and Big Data Engineering Research Center of Jilin Province(2020C005).
文摘To solve the problem of difficulty in identifying apple diseases in the natural environment and the low application rate of deep learning recognition networks,a lightweight ResNet(LW-ResNet)model for apple disease recognition is proposed.Based on the deep residual network(ResNet18),the multi-scale feature extraction layer is constructed by group convolution to realize the compression model and improve the extraction ability of different sizes of lesion features.By improving the identity mapping structure to reduce information loss.By introducing the efficient channel attention module(ECANet)to suppress noise from a complex background.The experimental results show that the average precision,recall and F1-score of the LW-ResNet on the test set are 97.80%,97.92%and 97.85%,respectively.The parameter memory is 2.32 MB,which is 94%less than that of ResNet18.Compared with the classic lightweight networks SqueezeNet and MobileNetV2,LW-ResNet has obvious advantages in recognition performance,speed,parameter memory requirement and time complexity.The proposed model has the advantages of low computational cost,low storage cost,strong real-time performance,high identification accuracy,and strong practicability,which can meet the needs of real-time identification task of apple leaf disease on resource-constrained devices.
基金supported by the Beijing Natural Science Foundation (L202003)National Natural Science Foundation of China (No. 31700479)。
文摘Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation signals.The proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed model.The proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to LWNet.Furthermore, the model also has a moderate computation complexity compared with several state-of-the-art methods.
基金supported by the National Natural Science Foundation of China(62371350,62171324,62471338,U1903214).
文摘Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image.Local features extracted by convolutions,etc.,capture finegrained details such as edges and textures,while global features extracted by full connection layers,etc.,represent the overall structure and long-range relationships within the image.These features are crucial for accurate object detection,yet most existing methods focus on aggregating local and global features,often overlooking the importance of medium-range dependencies.To address this gap,we propose a novel full perception module(FPModule),a simple yet effective feature extraction module designed to simultaneously capture local details,medium-range dependencies,and long-range dependencies.Building on this,we construct a full perception head(FP-Head)by cascading multiple FP-Modules,enabling the prediction layer to leverage the most informative features.Experimental results in the MS COCO dataset demonstrate that our approach significantly enhances object recognition and localization,achieving 2.7−5.7 APval gains when integrated into standard object detectors.Notably,the FP-Module is a universal solution that can be seamlessly incorporated into existing detectors to boost performance.The code will be released at https://github.com/Idcogroup/FP-Head.
文摘针对复杂海洋环境中存在背景噪声、海洋垃圾特征模糊和目标尺度小的检测挑战,本文提出一种基于改进CenterNet的海洋垃圾无锚检测算法——MG-CenterNet。引入GB(green-blue)注意力机制,通过关注海洋图像绿色、蓝色通道来增强特征提取;利用跨层特征聚合(cross-layer feature aggregation,CFA)模块丰富关键特征反馈,使模型获取更多像素级语义信息从而精准分类图像;构造完全交并比(complete intersection over union,CIoU)损失函数优化边界框匹配度,进一步提高目标定位精度。MG-CenterNet在TrashCan数据集和自建数据集上分别取得了77.98%和76.92%的平均精确率均值(mean average precision,m AP),推理速度分别达到27.18帧/s和26.98帧/s。研究结果证明MG-CenterNet在检测精度上显著优于其他算法,满足实时检测的要求。低对比度及遮挡条件下的验证实验进一步证明了所提出算法的鲁棒性和可靠性,为复杂环境中的海洋垃圾检测提供了科学参考。
基金Project supported by the National Key R&D Program of China(No.2020YFF01015000ZL)the Fundamental Research Funds for the Central Universities,China(No.3072022CF0806)。
文摘To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results,researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques.To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios,we have designed a novel modulation recognition network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing.The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with multi-scale feature fusion.The two modules are skip-connected to work together to improve the robustness of the overall network.Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods.The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.
