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Research on infrared image classification of power equipment based on YOLOv7 3

Research on infrared image classification of power equipment...

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3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Chen, Jia Yu, Chengbo Wang, Shibin He, Xin Zhang, Wei Jiang, Qichao College of Electrical and Electronic Engineering Chongqing University of Technology Chongqing 400054 China State Grid Chongqing Shinan Electric Power Supply Branch Chongqing401336 China

ISBN: (纸本)9781510668522

To address the problems of low accuracy and slow detection speed of traditional classification methods for infrared images of power equipment, the YOLOv7 model is used in this paper for the classification task of infrared images of power equipment. Firstly, the data set is labeled according to the existing data set, and then the data set is put into YOLOv7 and YOLOv5s network for comparison. The experimental results show that the YOLOv7 model has higher accuracy and faster detection speed in the power equipment infrared image classification task, and its average accuracy is 91.7%, which is higher than the average accuracy of 86.6% of YOLOv5s, and the model can detect blurred infrared images in power scenes, which has good potential for application. This study provides an effective solution in the field of infrared image classification of power equipment, which can play an important role in the fields of power inspection and fault diagnosis © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image classification

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Controllable image subtitle generation in electric power construction scenes based on codec 4

Controllable image subtitle generation in electric power con...

引用

4th international conference on advanced algorithms and signal image processing, aasip 2024

作者： Zhu, Bing Zhu, Bin Li, Juan Shenyang Electric Power Survey & Design Institue Co. Ltd Shenyang China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

In the context of power construction, image caption generation leverages deep learning-based encoding and decoding techniques to comprehend image information and convert it into textual descriptions. This approach enhances traditional image analysis by providing preemptive safety warnings and diversifying the output formats. Conventional methods for image caption generation lack controllability and detailed descriptions, and there is a paucity of research focused on image descriptions in power construction scenarios. To address this, this paper proposes a controllable image caption generation optimization method based on an encoder-decoder architecture. A novel feature extraction model is introduced, utilizing the FVCR-CNN (faster and visual commonsense region-convolutional neural network) as the encoder to extract salient and visual commonsense features from the images. The activation function is improved to develop an enhanced M-tanh-based long short-term memory (MT-LSTM) neural network for feature decoding. Finally, a multi-branch decision strategy is employed to optimize the output. The proposed method was trained and tested on a dataset of power scene descriptions using the Ubuntu 16.04 and PyTorch deep learning framework. Experimental results demonstrate a significant improvement in the accuracy of image caption generation, as well as enhanced controllability of scene descriptions, thereby substantially improving the intelligent level of safety management at power construction sites. © 2024 SPIE.

关键词： Convolutional neural networks

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Speech visualization for hearing impairments: From speech to colour image sequence combining text and meaning 3

Speech visualization for hearing impairments: From speech to...

引用

3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Shi, Xiaoyan Wang, Xu College of Information Science and Engineering Northeastern University Shenyang110819 China

ISBN: (纸本)9781510668522

Hearing-impaired children have difficult in learning and understand the active speakers. In particular, pronunciation isn't related to character form. In order to enhance the communication between the hearing impairments and active speakers in this situation, this paper presents a new visible speech mode without speech recognition, which uses a color image sequence combining text and meaning to represent the pronunciation of a tonal word. First, the high-dimension combined feature, which consists of tone and Mel frequency cepstrum coefficient (MFCC) features extracted from speech signals, is mapped into a low-dimensional feature by an audio encoder. Second, due to the outputs of an image encoder, the low-dimensional feature is decoded as tone image, word-no-Tone image, pictograph image, and meaning image in word decoder and image generator. Finally, an image sequence containing four types of images are synthesized and overlaid on colors due to the formant frequencies. When this is done, the text, semantics, and sound characteristics of the pronunciation of a tonal word are integrated into a color image sequence. In our visible speech experiments and perceptual tests, the recognition accuracy and subjective mean scores are all above 87%. The results show that our speech-To-image sequence mode is effective and feasible. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Color

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image enhancement algorithm for crystallization state detection of penicillin salt solution 3

Image enhancement algorithm for crystallization state detect...

引用

3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Xiaojun, Wang Yue, Du Yan, Wang Department of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China National Pharmaceutical Group Weiqida Pharmaceutical Co.Ltd Datong037000 China

ISBN: (纸本)9781510668522

The crystalline state detection of penicillin salt solution is performed on the basis of the acquired images of penicillin salt solution, and in order to improve the accuracy of subsequent detection, an image enhancement algorithm for crystalline state detection of penicillin salt solution is proposed for the problem of poor quality of penicillin salt solution images. Firstly, in order to remove the disturbing factors in the penicillin solution image and extract its target solution region, an automatic image segmentation algorithm based on OTSU algorithm and mathematical morphological processing is proposed;secondly, using the characteristics of three independent channels in HSV space, a linear grayscale transformation and mean filtering method is designed for the H component to realize the color adjustment of the target solution region, and the V component is used to enhance the image brightness by Finally, the enhanced target solution region is obtained by combining the components, and the final enhanced image is obtained by spatial transformation and mapping. The experimental results show that after the algorithm processing, the accuracy of crystallization detection of penicillin solution in uncrystallized state and crystallized state reaches 99.32% and 99.40% respectively, which is 30.72% and 9.22% higher than the accuracy before enhancement, which can effectively improve the accuracy of crystallization detection of penicillin solution. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image enhancement

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Low-light image enhancement based on conditional generative adversarial network

Low-light image enhancement based on conditional generative ...

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2021 international conference on advanced algorithms and signal image processing, aasip 2021

作者： Li, Huaji Cheng, Jianghua Liu, Tong Cheng, Bang Liu, Zilong College of Electronic Science National University of Defense Technology Hunan Changsha410073 China 32065Troops Liaoning Shenyang110162 China

We present an end-to-end low-light image enhancement learning method. This learning is based on the conditional generative adversarial networks(GAN) and realizes low-light images enhancement. Specifically, our method uses a convolutional neural network containing residual structures as a generator and WGAN-GP as a discriminator to generate an effective low-light enhancement model under the constraints of GAN loss, Perceptual loss and Structural similarity loss. The model can retain the detailed information of the original image, improve the brightness of the image without generating noise interference, while the generated images are more natural and have higher quality. Extensive experimental results show that our method has reached the state-of-art in multiple objective evaluation indicators of image quality, and the visual appearance is superior. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： image enhancement

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A pixel affinity adaptive expansion weakly supervised skin image segmentation method 3

A pixel affinity adaptive expansion weakly supervised skin i...

引用

3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Huang, Shan Fan, Huijie Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shen Yang China

ISBN: (纸本)9781510668522

image annotation of medical scenes is expensive, then weak supervision methods have emerged. Therefore, this paper proposes a pixel affinity adaptive expansion model for medical image label generation in skin scenes. The method is divided into two stages. In the first stage, initial pseudo label is generated based on the characteristics of the skin image using the proposed self-Adaptation expansion module. In the second stage, the generated initial pseudo label uses the characteristics of the pixel pair to distinguish the foreground background and class boundaries to generate the final label. Finally, the generated tags are used for fully supervised segmentation. Experiments were conducted on the ISIC 2016 and ISIC 2017 datasets, respectively. The MIoU of the proposed method was 63.24% and 50.43% on the two datasets, while the Dice-score was 76.13% and 67.79%, the accuracy rate was 81.28% and 64.79%, and the recall rate was 73.54% and 71.03%, which is higher than most existing weakly supervised semantic segmentation algorithms, and achieved Sota (State of the arts) on the two skin datasets. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Semantics

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UAV image stabilization algorithm based on improved Shi-Tomasi 3

UAV image stabilization algorithm based on improved Shi-Toma...

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3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Shi, Xiaoquan Peng, Zhangjun Liu, Zhigui Li, Li Liu, Daoguang Luo, Zhixin School of Information Engineering Southwest University of Science and Technology No. 59 Middle Qinglong Avenue Fucheng District MianYang China

ISBN: (纸本)9781510668522

As the UAV is susceptible to its own engine vibration and irregular airflow in the inspection process, the image sequence collected has jitter, which affects the reliability of the inspection results. To solve this problem, this paper proposes an improved Shi-Tomasi algorithm combined with a selective mechanistic motion model for UAV inspection image stabilization algorithm to eliminate the jitter component of images and produce stable images. Firstly, the feature points in the image are extracted by using the improved Shi-Tomasi algorithm, and feature point matching is performed by using optical flow tracking. Then the motion model is determined to describe the inter-frame motion by a selection mechanism based on the matched feature point pairs. The obtained original motion trajectories are turned into smooth trajectories by sliding filtering, and finally stable images are generated by warping and cropping. The experimental data use the UAV inspection image sequences collected by ourselves. The experimental results demonstrate the good performance of the proposed algorithm, which can quickly and effectively eliminate the image dithering. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Unmanned aerial vehicles (UAV)

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Auto thresholds of image multi-level segmentation applying maximum value to construct 2 d histogram 4

Auto thresholds of image multi-level segmentation applying m...

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4th international conference on advanced algorithms and signal image processing, aasip 2024

作者： Zhang, Yunfei Zhang, Zhan Xu, Ri Wang, Ping He, Xianghong College of General Education Guangdong University of Science and Technology Guangdong Dongguan523083 China Co. Ltd. Xicheng District Beijing10020 China Department of Planning and Development Avic Composite Corporation Ltd. Aviation Industrial Park Shunyi District Beijing100300 China

ISBN: (数字)9781510682818

ISBN: (纸本)9781510682801

It may achieve auto segmentation to construct 2 D histogram utilizing 2 D entropy. Habitual approach practices center gray scale and average scale of points in the same 4 neighbor. Actually, the horizontal or vertical change of the gray scale will not stand for the whole natures among neighbor points also. A maximum value way is constructed to established 2 D histogram by utilizing of center gray value and maximum value of 4 other point gray values except 4 neighbor points in the same 3×3 neighbor. The effects of multi-level experiments to almost all images in Matlab image toolbox manifest the validity of the constructed way. © 2024 SPIE.

关键词： image segmentation

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Ensemble of deep convolutional neural networks using multi-spectral vision transformer for diabetic macular edema classification from multicolor image 3

Ensemble of deep convolutional neural networks using multi-s...

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3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Cui, Min Gao, Xinke Song, Jianyi Zhang, Ping Jinan Vocational College Jinan250103 China

ISBN: (纸本)9781510668522

Diabetic Macular Edema (DME) is a prevalent condition that threatens vision. Early and accurate detection of DME, coupled with appropriate treatment, can reduce the risk of blindness for patients. Multicolor image (MCI) facilitates the diagnosis of DME by providing multiple spectral images of retinal structures. Recent advances in deep learning methods for multimodal analysis have demonstrated superior performance over manual analysis. However, existing techniques for DME classification still suffer from low accuracy due to the insufficient utilization of MCI's properties. To address this issue, we propose a Multi-Spectral Vision Transformer (MSVT) model as a potential solution. Our model first resizes images from different spectra and then splits them into image patches. These patches are then projected into linear embeddings and added with positional embeddings. The Transformer encoder is then applied for feature extraction. The features of each spectrum are processed by a Multilayer Perceptron (MLP) head and then fused by a feature fusion module. Finally, the features are compressed and fed into a classifier for classification. We evaluate the empirical performance of the proposed algorithm on our in-house datasets. The classifier achieves a prediction accuracy of 0.935, sensitivity of 0.912, specificity of 0.924, and an AUC of 0.938 for predicting the DME status of MCIs. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image classification

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Robust network based on embedded convolutional block attention module and earth mover's distance for few-shot learning 3

Robust network based on embedded convolutional block attenti...

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3rd international conference on advanced algorithms and signal image processing, aasip 2023

作者： Zhang, Tong Xin, Min Zhang, Yinan Liao, Jiashe Xie, Qingfeng School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510668522

Deep neural networks of training require a large quantity of effectively labeled datasets, and models trained with insufficient samples will cause a variety of problems, such as overfitting and poor generalization ability. Few-shot learning has been proposed to settle the problem, and the methods introduced in some papers have achieved impressive results. However, most of the methods have poor robustness due to the under-utilization of the global information and local information of the image. For solving the above problems, an improved classification network is proposed in this paper, which introduces two modifications. On the one hand, Convolutional Block Attention Module (CBAM) is embedded in the network for extracting global features, thus the model can quickly suit various tasks. On the other hand, the image is randomly divided into tiles and represented as a set of local features, the Earth Mover's Distance (EMD) is employed to calculate the distance between embedded sets of two images. Finally, the weighted sum of these two parts' classification errors, is computed as the total classification error of the proposed model. Experiments are conducted on two benchmark datasets: mini-imageNet and tiered-imageNet, and the results demonstrate that our proposed method achieves state-of-Theart(SOTA) results in the field of few-shot image classification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： image classification

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