Applications of machine vision in agricultural robot navigation: A review


Purpose
The purpose of this study is to provide an in-depth understanding about the indoor-orbital electrical inspection robot, which is useful for motivating the further investigation on the inspection of electrical equipment. Currently, electric energy has a strong correlation with the economic development of the country. Intelligent substations play an important role in the transmission and distribution of the electricity; the maintenance of the substation has attracted intensive attention due to the requirement of reliability and safety. The indoor-orbital electrical inspection robot has increasingly become the main tool to realize the unmanned. Hence, a systematic review is conducted systematically reviewing the current technical status of the indoor-orbital electrical inspection robot and discuss the existed problems.


Design/methodology/approach
In this paper, the most essential achievements in the field of indoor-orbital electrical inspection robots were reported to present the current status, and the mechanical structures and key inspective technologies were also discussed.


Findings
Four recommendations are provided from the analyzed review, which have made constructive comments on the overall structural design, functionality, intelligence and future development direction of the indoor-orbital electrical inspection robot, respectively.


Originality/value
To the best of the authors’ knowledge, this is the first systematic review study on indoor-orbital electrical inspection robots; it fills the theoretical gap and proffers design ideas and directions for the development of the indoor-orbital electrical inspection robot.


A review of indoor-orbital electrical inspection robots in substations

With the development of the information age, the layout image is no longer a simple combination of text and graphics, but covers the complex layout image obtained from text, graphics, images and other layout elements through the process of artistic design, pre-press processing, typesetting, and so on. At present, the field of aesthetic-quality assessment mainly focuses on photographic images, and the aesthetic-quality assessment of complex layout images is rarely reported. However, the design of complex layout images such as posters, packaging labels, advertisements, etc., cannot be separated from the evaluation of aesthetic quality. In this paper, layout analysis is performed on complex layout images. Traditional and deep-learning-based methods for image layout analysis and aesthetic-quality assessment are reviewed and analyzed. Finally, the features, advantages and applications of common image aesthetic-quality assessment datasets and layout analysis datasets are compared and analyzed. Limitations and future perspectives of aesthetic assessment of complex layout images are discussed in relation to layout analysis and aesthetic characteristics. 

Research Progress on the Aesthetic Quality Assessment of Complex Layout Images Based on Deep Learning

: Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice similarity coeﬀicient, mean intersection of union, boundary displacement error (BDE), Hausdorff distance, and receiver operating characteristic (ROC). Compared to results from the literature, XAIRL shows the highest overall performance, achieving accuracy of 0.93, precision of 0.91, recall of 0.95, and F1 score of 0.93. XAIRL also displays the highest IoU, Dice similarity coeﬀicient, and ROC curve and the lowest BDE and Hausdorff distance. Although U-Net performs well for most metrics, Mask R-CNN shows slightly worse performance but still outperforms the random forest and support vector machine algorithms. By building a high-quality foot infrared imaging dataset, machine learning-based algorithms can accurately analyze foot temperature and pressure distribution. These models can then be used to customize shoes for individual wearers, improving their comfort and reducing the risk of foot injuries, particularly for those with high blood pressure.

Infrared imaging segmentation employing an explainable deep neural network

Medical imaging plays an important role in medical diagnosis and treatment. It is also useful in medical applications. The proposed concept's goal is to understand the importance of data balancing, data augmentation, and segmentation in the clinical field, to improve image data balancing using data augmentation and edge detection techniques, to improve radiology image preprocessing to locate regions of interest (ROI), and to construct custom-built Deep Neural Networks (DNN) in diagnosing respiratory illness using Machine Learning approaches. Images of varying quality from multiple machine types are frequently included in different datasets. This study used four datasets, three of which are online datasets from Kaggle and the fourth is real-time radiology pictures of COVID and Pneumonia-infected persons from neighboring local hospitals. We proposed RESP_DATA_BALANCE for image data balance in dataset construction, and RDD_ROI (Respiratory Disease Detection Region of Interest) algorithm, which combines improved image feature extraction technique using a GLCM and unsupervised K-means clustering for segmentation to identify the region of interest in the detection of respiratory diseases. Our suggested custom-built 28-layer Respiratory Disease Detection Deep Neural Network (RDD_DNN) is used for further training, testing, and validation. Furthermore, experimental results focus on performance characteristics using various data augmentation, edge detection, and preprocessing strategies.  The experimental purpose of our research study is to aid in the classification and early diagnosis of respiratory disorders.

pdf/radiology-image-data-augmentation-and-image-enhancement-in-y75qf1mcu1.pdf

Radiology Image Data Augmentation and Image Enhancement in Respiratory Disease Infection Detection Using Machine Learning Approach

Image segmentation is a hot and difficult research topic in the fields of pattern recognition and computer vision. At present, the method based on support vector machine has been widely used in image segmentation, but the selection of training samples mostly depends on manual selection. This reduces the adaptability of image segmentation, but also affects the classification performance of support vector machine. A new image segmentation method based on support vector machine (SVM) is proposed, which combines the advantages of mean clustering algorithm to automatically obtain training samples, then extracts image color features and texture features respectively, and uses them as training samples of SVM. Finally, the trained classifier is used to segment the image. The experimental results show that the proposed algorithm is better than the traditional support vector machine-based algorithm.

A New Image Segmentation Method Based on Support Vector Machine

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A New Image Segmentation Method Based on Support Vector Machine