Mr. Huimin Guo Profile

Huimin Guo

at Beijing Aerospace Feiteng Equipment and Technology Co., Ltd.

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Publications (3)

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Proceedings Article | 12 March 2021 Paper

Detection method of tubular target leakage based on deep learning

Donghao Luo, Dong Wang, Huimin Guo, Xiaoxia Zhao, Meiling Gong, Lin Ye

Proceedings Volume 11763, 1176384 (2021) https://doi.org/10.1117/12.2587554

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Aiming at the problem that the previous tubular target leak detection method is not sensitive to small leakage and slow leakage and false alarm caused by external interference is not strong, tubular target leakage detection method based on deep learning is proposed. Firstly, the target detection network YOLO3 model is used to detect the tubular target in optical images. By analyzing the test results, the YOLO3 based tubular target leakage detection network is optimized and improved from three aspects: data set expansion, detection mode and network structure. Including: 1) using data transformation using rotation transformation, color dithering, zoom transformation, shift transformation and flip transformation on the data set; 2) according to the characteristics of the tubular target images, the detection method of polygon frame selection is used; 3) simplifying the network structure of the detection and output part. Finally, the improved network is trained and verified. The experimental results show that compared with the YOLO3 network model, the recognition accuracy and recall rate of the tubular target and the leaked area are greatly improved, and the average detection time is also reduced.

Proceedings Article | 12 March 2021 Paper

A review of non-maximum suppression algorithms for deep learning target detection

Meiling Gong, Dong Wang, Xiaoxia Zhao, Huimin Guo, Donghao Luo, Min Song

Proceedings Volume 11763, 1176332 (2021) https://doi.org/10.1117/12.2586477

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Deep learning methods have been more and more widely applied in the field of target detection. As an important part of deep learning target detection, non-maximum suppression is used to eliminate redundant detection bounding boxes generated during target detection and find out the optimal target boundary boxes, so as to speed up detection efficiency and improve detection accuracy. This article first introduces the related concepts and computational principles of traditional non-maximum suppression algorithm, and points out its problems. Based on this, the Soft-NMS, Softer-NMS, IOU-Guided NMS, Adaptive NMS and DIOU-NMS, a total of 5 kinds of improved maximum suppression algorithm principle is introduced and comparative analysis. And then we summarize the advantages and disadvantages of various algorithms. Finally, in view of the common problems existing in each algorithm, this paper points out the direction for improvement of non- maximum suppression algorithm, and provides technical reference and support for researchers in related fields.

Proceedings Article | 31 January 2020 Paper

Leaf blast spot detection method based on Linknet

Chen Junshen, Gong Meiling, Dong Wang, Xiaoxia Zhao, Min Song, Huimin Guo

Proceedings Volume 11427, 1142742 (2020) https://doi.org/10.1117/12.2553085

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This paper aims to solve the problem of automatic detection of rice leaf lesions in natural scenes using deep learning techniques. In this paper, the Linknet full convolutional network was built to train the segmentation model. The network compensates the lost spatial information in the feature extraction process through the short connection structure between downsampling and corresponding upsampling. The model takes rice canopy RGB image as input and then output binarized lesion segmentation image. Then considered with the distribution characteristics of lesion spots, the loss function of the origin model was replaced with Focal loss function, which further improved the segmentation accuracy of the model. The average precision and recall have respectively achieved 98.55% and 98.64% on validate data set, and the average false positive rate has reduced to 1.36%, which has a better segmentation performance. It creates a good precondition for automatic identification of leaves diseases.

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