Detection and recognition of moving video objects: Kalman filtering with deep learning

© 2021. All rights reserved. Research in object recognition has lately found that Deep Convolutional Neuronal Networks (CNN) provide a breakthrough in detection scores, especially in video applications. This paper presents an approach for object recognition in videos by combining Kalman filter with CNN. Kalman filter is first applied for detection, removing the background and then cropping object. Kalman filtering achieves three important functions: predicting the future location of the object, reducing noise and interference from incorrect detections, and associating multi-objects to tracks. After detection and cropping the moving object, a CNN model will predict the category of object. The CNN model is built based on more than 1000 image of humans, animals and others, with architecture that consists of ten layers. The first layer, which is the input image, is of 100 * 100 size. The convolutional layer contains 20 masks with a size of 5 * 5, with a ruling layer to normalize data, then max-pooling. The proposed hybrid algorithm has been applied to 8 different videos with total duration of is 15.4 minutes, containing 23100 frames. In this experiment, recognition accuracy reached 100%, where the proposed system outperforms six existing algorithms

Hybrid mamdani fuzzy rules and convolutional neural networks for analysis and identification of animal images

Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052

Hydrophobicity Classification of RTV Silicone Rubber-Coated Insulators Using Deep Learning Algorithms

Silicone rubber-based outdoor polymeric insulators are widely employed in electric power transmission and distribution networks to replace conventional ceramic insulators, owing to their superior performance in contaminated and wet environments. Silicone rubber (SIR) insulators offer several advantages like high hydrophobicity, low cost, vandalism resistance, and lightweight. However, when exposed to electrical (dry band arcing and partial discharge) and environmental stresses (humidity, ultraviolet radiation, acid rain and pollution) they suffer from different forms of aging. The first form of aging is the temporal loss of hydrophobicity. However, SIR insulators can recover the hydrophobicity property due to the diffusion of the low molecular weight (LMW) from the bulk of the insulating material to the insulators’ surface. Hence, it is important to classify the hydrophobicity status of SIR insulators as an indication of the aging degree. Different methods have been implemented to classify the hydrophobicity of the insulator surface including static contact angle measurement, dynamic contact angle measurement, and hydrophobicity class (HC). The later technique is the most practical method that can be used in the field and can assess a wide surface area. The surface wetting tendency is manually classified using one of six classes, i.e. HC1-HC6, where HC1 refers to a completely hydrophobic surface and HC6 is a completely hydrophilic surface. The main objective of this thesis is to automatically assess the hydrophobicity classes of non-ceramic insulators under a variety of conditions using deep learning techniques. A dataset of hydrophobicity classes (HC1-HC6) was created and prepared including 4197 images each having 2242×24 pixels size to train the proposed model. Several deep learning techniques, including Convolutional Neural Networks (CNN), Transfer Learning (TL), and Object Detection (OD), were used in this thesis to categorize and assess the hydrophobicity classes of ceramic insulators coated with room temperature vulcanized silicone rubber (RTV-SIR). MobileNet model was found to have the highest accuracy and less training time after comparing with other CNN pre-trained models. This model was then trained and tested under several conditions, including indoor, bright, and dark lighting conditions, and achieved accuracy of 97.77%, 89.44%, and 95%, respectively. Moreover, the proposed model achieved a recognition rate of 96.11% when tested on a full-scale silicone rubber insulator. The developed model was then deployed as a web application for convenience in the assessment of hydrophobicity classes. The proposed model could be utilized to evaluate SIR insulators surface conditions in an effective and automatic way under different conditions

Data for: Hydrophobicity Classification of Composite Insulators Based on Convolutional Neural Networks

By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Hydrophobicity Classification of Composite Insulators Based on Convolutional Neural Networks

By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Hydrophobicity Classification of Composite Insulators Based on Convolutional Neural Networks

By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes of composite insulators using distilled water-ethyl alcohol as spraying solution.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Hydrophobicity Classification of Composite Insulators Based on Convolutional Neural Networks

By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Data for: Hydrophobicity Classification of Composite Insulators Based on Convolutional Neural Networks

By applying the spray method (IEC Standard 62073), about 4500 photos were collected and are available online, from all hydrophobicity classes using distilled water-ethyl alcohol as spraying solution. The pictures of the seven different hydrophobicity classes were split into three separate sets for each hydrophobicity class. The first one consisting of 400 instances of each class (400 × 7 = 2800 photos) was used for the training of the networks. The second one consisting of 100 instances of each class (100 × 7 = 700 photos) was used for the evaluation-validation of the learning course and the comparison of the different models. The last one with 122-165 different instances of each class (980 photos) was used for the final assessment of our chosen model