Improving Neural Network Detection Accuracy of Electric Power Bushings in Infrared Images by Hough Transform

Zhao, Huiru; Zhang, Zeyan

doi:10.3390/s20102931

Cited by 15 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Zhao proposed to incorporate medical prior information into image enhancement [22], judge the brightness threshold of the target area through medical knowledge, and process the image with this threshold to obtain an enhanced image, thereby improving the accuracy of the model. Zhao proposed a method combining Hough transform and Yolo9000 [23,24], using Hough transform and image rotation to determine the optimal recognition angle for target detection, to solve the problem of poor rotation invariance of Yolo9000. In the field of rotating object detection, the angle information of rotation is also widely fused.…”

Section: Target Detection Model Fused With Prior Informationmentioning

confidence: 99%

Ellipse shape prior based anti-noise network for parathyroid detection

Liu

Cai

Chen

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

View full text Add to dashboard Cite

Endoscopic thyroid surgery is a common treatment method for thyroid diseases. However, due to the small size of parathyroid gland, its unstable position during surgery, and the similar color and texture of the parathyroid gland to its surrounding tissues and organs, parathyroid glands are extremely difficult to be visualized and detected during the operation, leading to accidental damages which result in abnormal parathyroid hormone secretion after surgery. Therefore, detecting the parathyroid glands and preventing damage during endoscopic thyroid surgery is critical. The current target detection technology using SOAT algorithm can help detecting the small parathyroid area, but the accuracy and efficiency are often unsatisfied because of the high image noise. In this research, we propose a PGNet model by applying an anti-noise feature extraction module to prevent the sharp drop in the accuracy of the model under the noisy environment. The anti-noise feature extraction module is based on residual structure, and hence the capability of parathyroid shape detection can be significantly improved. To further improve the detection accuracy of the parathyroid gland, an ellipse anchor frame and an improved IoU loss function have been applied in this PGNet model. The detection accuracy of PGNet model has been verified using data obtained in real-time surgical scenes, and the results show that the PGNet model can achieve 89.3% parathyroid detection accuracy when the IoU threshold is 0.5, which is better than the current SOTA CNN model.

show abstract

Section: Target Detection Model Fused With Prior Informationmentioning

confidence: 99%

Ellipse shape prior based anti-noise network for parathyroid detection

Liu

Cai

Chen

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

View full text Add to dashboard Cite

show abstract

“…Here, we used HT to extract features from the word images; more specifically, the vertical segments of the word images, obtained by the process as described in Section 3.2 . We should mention that many researchers have used HT to extract features for several image processing and pattern recognition tasks, such as finding strokes in geoscientific images [ 48 ], mammogram classification for early detection of breast cancer [ 49 ], face recognition [ 50 ], contextual line feature extraction for semantic line detection [ 51 ], detection of electric power bushings from infrared images [ 52 ], and many more. In general, in such applications, HT is used to find the straight lines in the image space.…”

Section: Present Workmentioning

confidence: 99%

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Kundu

Malakar²,

Geem

et al. 2021

Sensors

View full text Add to dashboard Cite

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.

show abstract

“…An insulator fault detection method based on spatial features of aerial images was proposed by [19] to identify flaws in ceramics. The recognition accuracy of thermal spots was improved by [20] by Hough Transform. The major contributions in infrared analysis are focused on the improvement of the accuracy in complex backgrounds and perform detection of hot spots caused by bad contacts.…”

Section: Infrared Thermal Analysis Applied To Pdmentioning

confidence: 99%

Assessment of Partial Discharges Evolution in Bushing by Infrared Analysis

Castro¹,

Lucas²,

Fernandes³

et al. 2021

The 8th International Electronic Conference on Sensors and Applications

View full text Add to dashboard Cite

The quality of power systems is related to their capability to predict failures, avoid stoppages, and increase the lifetime of their components. Therefore, science has been developing monitoring systems to identify failures in induction motors, transformers, and transmission lines. In this context, one of the most crucial components of the electrical systems is the insulation devices such as bushings, which are constantly subjected to dust, thermal stresses, moisture, etc. These conditions promote insulation deterioration, leading to the occurrence of partial discharges. Partial discharges are localized dielectric breakdown that emits ultra-violet radiation, heat, electromagnet, and acoustics waves. The most traditional techniques to identify these flaws on bushings are based on the current, ultra high frequency, and acoustic emission analysis. However, thermal analysis stands out as a noise-resistant technique to monitor several components in the power systems. Although the thermal method is applied to detect different types of faults, such as bad contacts, overloads, etc, this technique has not been previously applied to perform partial discharge detection and evaluate its evolution on bushings. Based on this issue, this article proposes two new indexes to characterize the discharge evolution based on the infrared thermal analysis: the area ratio coefficient and the Red, Green, and Blue (RGB) ratio coefficient. Seven discharge levels were induced in a contaminated bushing, and an infrared thermal camera captured 20 images per condition, totalizing 140 images. New coefficients were used to perform the identification of discharge evolution. Results indicated that values of the new indexes increase with the partial discharge activity. Thus, the new imaging processing approach can be a promising contribution to literature, improving the reliability and maintenance planning for power transmission systems.

show abstract

Improving Neural Network Detection Accuracy of Electric Power Bushings in Infrared Images by Hough Transform

Cited by 15 publications

References 38 publications

Ellipse shape prior based anti-noise network for parathyroid detection

Ellipse shape prior based anti-noise network for parathyroid detection

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting

Assessment of Partial Discharges Evolution in Bushing by Infrared Analysis

Contact Info

Product

Resources

About