Effect of Spatial Filtering on Object Detection with the SURF Algorithm

Tomaszewski, Michał; Osuchowski, Jakub; Debita, Łukasz

doi:10.1007/978-3-319-75025-5_12

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…Ref. [ 53 ] commented on the development of a dictionary of local features that would allow the detection and subsequent localization of damage in various types of power network elements on digital images taken during inspections carried out by flying devices (airplanes, helicopters, UAVs) or brigades and service vehicles on the ground. Another study showed the evaluation of profiles representing an insulator, characterized by regular patterns [ 45 ].…”

Section: Power Line Elements Datasets Reviewmentioning

confidence: 99%

Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure

Ruszczak,

Michalski,

Tomaszewski

2023

Sensors

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The power sector is one of the most important engineering sectors, with a lot of equipment that needs to be appropriately maintained, often spread over large areas. With the recent advances in deep learning techniques, many applications can be developed that could be used to automate the power line inspection process, replacing previously manual activities. However, in addition to these novel algorithms, this approach requires specialized datasets, collections that have been properly curated and labeled with the help of experts in the field. When it comes to visual inspection processes, these data are mainly images of various types. This paper consists of two main parts. The first one presents information about datasets used in machine learning, especially deep learning. The need to create domain datasets is justified using the example of the collection of data on power infrastructure objects, and the selected repositories of different collections are compared. In addition, selected collections of digital image data are characterized in more detail. The latter part of the review also discusses the use of an original dataset containing 2630 high-resolution labeled images of power line insulators and comments on the potential applications of this collection.

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Section: Power Line Elements Datasets Reviewmentioning

confidence: 99%

Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure

Ruszczak,

Michalski,

Tomaszewski

2023

Sensors

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“…The quality of prediction generated by convolutional neural networks depends to a large extent on the use of an appropriate machine learning dataset [8]. The dataset built to solve a specific problem should be characterized by the following features [9,10]:…”

Section: Dataset Preparation For Cnnmentioning

confidence: 99%

Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

2020

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This article presents an analysis of the effectiveness of object detection in digital images with the application of a limited quantity of input. The possibility of using a limited set of learning data was achieved by developing a detailed scenario of the task, which strictly defined the conditions of detector operation in the considered case of a convolutional neural network. The described solution utilizes known architectures of deep neural networks in the process of learning and object detection. The article presents comparisons of results from detecting the most popular deep neural networks while maintaining a limited training set composed of a specific number of selected images from diagnostic video. The analyzed input material was recorded during an inspection flight conducted along high-voltage lines. The object detector was built for a power insulator. The main contribution of the presented papier is the evidence that a limited training set (in our case, just 60 training frames) could be used for object detection, assuming an outdoor scenario with low variability of environmental conditions. The decision of which network will generate the best result for such a limited training set is not a trivial task. Conducted research suggests that the deep neural networks will achieve different levels of effectiveness depending on the amount of training data. The most beneficial results were obtained for two convolutional neural networks: the faster region-convolutional neural network (faster R-CNN) and the region-based fully convolutional network (R-FCN). Faster R-CNN reached the highest AP (average precision) at a level of 0.8 for 60 frames. The R-FCN model gained a worse AP result; however, it can be noted that the relationship between the number of input samples and the obtained results has a significantly lower influence than in the case of other CNN models, which, in the authors’ assessment, is a desired feature in the case of a limited training set.

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“…In the man-made feature-based methods, multiple features including color [11,14], shape [15][16][17], edge [18,19], gradient [20], texture [21], key-points [22][23][24][25] and their fusions [26,27] have been explored. Meanwhile, some mathematical models have also been applied, such as the snake model [28], Hough transform [29], Active Contour Model [30], Fuzzy c-means [31], and Receptive field model [32].…”

Section: Appl Sci 2019 9 X For Peer Review 2 Of 22mentioning

confidence: 99%

“…However, this method is time-consuming and far from a practical application. In the work of [22,24,25], the key-point features of the insulator are analyzed. Subsequently, the SURF or DoG (Difference of Gaussians) key-point features are used to locate the insulators in an aerial image.…”

Section: Appl Sci 2019 9 X For Peer Review 2 Of 22mentioning

confidence: 99%

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

et al. 2019

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Insulator faults detection is an important task for high-voltage transmission line inspection. However, current methods often suffer from the lack of accuracy and robustness. Moreover, these methods can only detect one fault in the insulator string, but cannot detect a multi-fault. In this paper, a novel method is proposed for insulator one fault and multi-fault detection in UAV-based aerial images, the backgrounds of which usually contain much complex interference. The shapes of the insulators also vary obviously due to the changes in filming angle and distance. To reduce the impact of complex interference on insulator faults detection, we make full use of the deep neural network to distinguish between insulators and background interference. First of all, plenty of insulator aerial images with manually labelled ground-truth are collected to construct a standard insulator detection dataset ‘InST_detection’. Secondly, a new convolutional network is proposed to obtain accurate insulator string positions in the aerial image. Finally, a novel fault detection method is proposed that can detect both insulator one fault and multi-fault in aerial images. Experimental results on a large number of aerial images show that our proposed method is more effective and efficient than the state-of-the-art insulator fault detection methods.

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Effect of Spatial Filtering on Object Detection with the SURF Algorithm

Cited by 8 publications

References 7 publications

Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure

Overview of Image Datasets for Deep Learning Applications in Diagnostics of Power Infrastructure

Evaluation of Power Insulator Detection Efficiency with the Use of Limited Training Dataset

A Method of Insulator Faults Detection in Aerial Images for High-Voltage Transmission Lines Inspection

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