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第三十九届中国控制会议

作者： Zhihao Chen Yewei Xiao Yan Zhou School of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University

Aiming at the direction of insulators are uncertain in the inspection images of transmission lines, and the problems of missed detection and inaccurate positioning due to complex backgrounds and occlusions, a method of insulators detection based on multi-angle region proposal network is proposed. Firstly, using a convolutional neural network, that characteristics of the different levels of features are obtained, and a feature fusion of shallow features and in-depth features is used to obtain richer features. Next, using the region proposal network based on Faster R-CNN, multi-angle factors are added and fine-tuned by non-maximum suppression so that it can generate candidate frames in any orientation to fit the target better. At the same time, the original pooling layer was changed to the RoIAlign layer. Finally, a 1 × 1 convolution layer is added before the fully connected layer of the network in order to reduce the parameters of its feature map and avoid overfitting. The inspection insulator image dataset is constructed and tested. The experimental results show that the final detection result obtained using this method can accurately detect the insulator, and its performance is better than the existing mainstream target detection algorithms and has high engineering application value.

关键词： Insulator Faster R-CNN multi-angle region proposal network RoIAlign

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Visual-simulation region proposal and generative adversarial network based ground military target recognition

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Defence Technology（防务技术） 2022年第11期18卷 2083-2096页

作者： Fan-jie Meng Yong-qiang Li Fa-ming Shao Gai-hong Yuan Ju-ying Dai Department of Space Test and Launch Noncommissioned Officer SchoolSpace Engineering UniversityBeijing102299China Department of Mechanical Engineering College of Field EngineeringArmy Engineering University of PLANanjing210007China

Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper, a recognition method, involving a novel visual attention mechanismbased Gabor region proposal sub-network(Gabor RPN) and improved refinement generative adversarial sub-network(GAN), is proposed. Novel central-peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset(GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.

关键词： Deep learning Biological vision Military application region proposal network Gabor filter Generative adversarial network

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A fruit detection algorithm based on R-FCN in natural scene 32

A fruit detection algorithm based on R-FCN in natural scene

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第32届中国控制与决策会议

作者： Liu Jian Zhao Mingrui Guo Xifeng School of Information and Control Engineering Shenyang Jianzhu University

ISBN: (数字)9781728158556

ISBN: (纸本)9781728158563

Aiming at the problem of poor precision and low efficiency of the vision system when agricultural robot picks fruit, this paper effectively fuses deep learning with machine vision, and proposes a new algorithm for fruit recognition and location, R-FCN, a deep learning algorithm combining regional suggestion network（RPN） and full convolutional neural network（FCN）. The proposed algorithm uses FCN to convolve the input image to achieve feature extraction at the pixel level. Among them, the fusion of residual network can make the deep network have more abundant feature information for fruit recognition, and deconvolution can realize the visualization of detection results. Using RPN generates multiple candidate boxes on the feature map after convolution operation, this can effectively separate the image foreground and background. Training was conducted on the public COCO data set, and testing experiments were conducted on the different states of three different fruits. The experimental results show that the algorithm in this paper improves the detection accuracy by 0.71% and 0.33% respectively compared with the previous algorithm in apple and orange recognition. It can also have a recognition accuracy of 82.30% for banana, which is a large-scale fruit. For different input images, it can realize the visualization of fruit recognition and location, reduce the influence of branches and leaves occlusion, enhance the robustness of the system, and improve the efficiency of picking.

关键词： Deep learning region proposal network Fully Convolutional network Target recognition and location

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Deep Learning Based Pedestrian Detection

Deep Learning Based Pedestrian Detection

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第30届中国控制与决策会议

作者： Weicheng Sun Songhao Zhu Xuewen Ju Dongsheng Wang School of Automatic Nanjing University of Posts and Telecommunications

The application of pedestrian detection is extremely extensive,such as in the field of intelligent surveillance,unmanned vehicle,and pedestrian *** the development of deep learning,deep learning based pedestrian detection method has greatly improved the accuracy of pedestrian detection;however,the computation cost is very *** this paper,a pedestrian detection framework based on deep learning is *** optimized PVANet is first utilized to generate the feature maps;then,the region proposal network is utilized to generate the pedestrian candidates and the corresponding scores;finally,the enhanced decision trees is utilized to complete the issue of pedestrian *** results conducted on Caltech pedestrian detection dataset demonstrate the effectiveness of our method.

关键词： Pedestrian Detection Pvanet Netwoek region proposal network Decision Trees

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