Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots

Kulik, Sergey; Shtanko, A. N.

doi:10.1016/j.procs.2020.02.129

Cited by 23 publications

(10 citation statements)

References 13 publications

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“…Convolutional Neural Network (CNN) is an algorithm that has become a tool for pattern recognition and object detection [8]. In this experiment, sheep species classification using the sheep faces extracting features on the sheep's faces.…”

Section: Table Of Contentmentioning

confidence: 99%

Sheep Face Classification using Convolutional Neural Network

Bimantoro

Emanuel

2021

2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT)

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Section: Table Of Contentmentioning

confidence: 99%

Sheep Face Classification using Convolutional Neural Network

Bimantoro

Emanuel

2021

2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT)

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“…Based on CNN (Convolution Neural Network), the visual data analysis can classify images, detect objects and generate images. CNN aims to reduce the complexity of the model and extract significant features by applying a convolution operation [17]. In visual data, object detection is a technique to find a candidate region for a detection target to recognize a specific target and to predict the type and location of the object (bounding box) [18,19].…”

Section: Image Object Detection Algorithmmentioning

confidence: 99%

“…The laser scan method is can measure general cracks as well as potholes, but the cost is very high [16]. In addition, a detection method using an image is a method that can detect a large area of potholes at a lower cost than the method using an acceleration sensor and a laser [17]. In images, however, it is difficult to detect potholes due to factors such as light and darkness and image quality.…”

Section: Introductionmentioning

confidence: 99%

Pothole Classification Model Using Edge Detection in Road Image

Baek

Chung

2020

Applied Sciences

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Since the image related to road damage includes objects such as potholes, cracks, shadows, and lanes, there is a problem that it is difficult to detect a specific object. In this paper, we propose a pothole classification model using edge detection in road image. The proposed method converts RGB (red green and blue) image data, including potholes and other objects, to gray-scale to reduce the amount of computation. It detects all objects except potholes using an object detection algorithm. The detected object is removed, and a pixel value of 255 is assigned to process it as a background. In addition, to extract the characteristics of a pothole, the contour of the pothole is extracted through edge detection. Finally, potholes are detected and classified based by the (you only look once) YOLO algorithm. The performance evaluation evaluates the distortion rate and restoration rate of the image, and the validity of the model and accuracy of the classification. The result of the evaluation shows that the mean square error (MSE) of the distortion rate and restoration rate of the proposed method has errors of 0.2–0.44. The peak signal to noise ratio (PSNR) is evaluated as 50 db or higher. The structural similarity index map (SSIM) is evaluated as 0.71–0.82. In addition, the result of the pothole classification shows that the area under curve (AUC) is evaluated as 0.9.

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“…The proposed method is used to improve the accuracy of the rotating bounding box. Kulik et al [18] addressed CV for intelligent robots by proposing a convolutional neural network for object detection that detects flags indicating unsatisfactory results for different objects. Training and the testing of objects is maintained throughout.…”

Section: Related Workmentioning

confidence: 99%

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

Alfarraj

Tolba

2020

Complex Intell. Syst.

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The computer vision (CV) paradigm is introduced to improve the computational and processing system efficiencies through visual inputs. These visual inputs are processed using sophisticated techniques for improving the reliability of human–machine interactions (HMIs). The processing of visual inputs requires multi-level data computations for achieving application-specific reliability. Therefore, in this paper, a two-level visual information processing (2LVIP) method is introduced to meet the reliability requirements of HMI applications. The 2LVIP method is used for handling both structured and unstructured data through classification learning to extract the maximum gain from the inputs. The introduced method identifies the gain-related features on its first level and optimizes the features to improve information gain. In the second level, the error is reduced through a regression process to stabilize the precision to meet the HMI application demands. The two levels are interoperable and fully connected to achieve better gain and precision through the reduction in information processing errors. The analysis results show that the proposed method achieves 9.42% higher information gain and a 6.51% smaller error under different classification instances compared with conventional methods.

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Using convolutional neural networks for recognition of objects varied in appearance in computer vision for intellectual robots

Cited by 23 publications

References 13 publications

Sheep Face Classification using Convolutional Neural Network

Sheep Face Classification using Convolutional Neural Network

Pothole Classification Model Using Edge Detection in Road Image

A two-level computer vision-based information processing method for improving the performance of human–machine interaction-aided applications

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