Saliency guided faster-RCNN (SGFr-RCNN) model for object detection and recognition

Sharma, Vipul; Mir, Roohie Naaz

doi:10.1016/j.jksuci.2019.09.012

Cited by 40 publications

(11 citation statements)

References 15 publications

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“…It involves image classification, object localization, object detection. Image classification method to find the object is belong to bolt class or screw class with the help of faster-RCNN classifier [16]. Then, next step is object localization is used to locate the fasteners object in the images based on the feature maps which is already gets trained with the help of RPN network.…”

Section: Object Recognitionmentioning

confidence: 99%

“…In addition to GDXray database, images of x-ray image modelling and image transformation methods are used to create the customized dataset. Further, certain augmentations like flip, random distortion, rotation, skew and zoom are also done to increase the size of the dataset [16]. Faster RCNN was evaluated on various architectures like with AlexNet, VGG-16, and ResNet-50,101 architectures.…”

Section: Introductionmentioning

confidence: 99%

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Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Karthikeyan

Subashini

2021

IJECE

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Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detection

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Section: Object Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Karthikeyan

Subashini

2021

IJECE

View full text Add to dashboard Cite

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“…In the case of transfer learning [36][37][38][39], the pre-trained models are applied in the solution of various problems by manipulating relevant layers of the network according to the new application's requirements. In this methodology, some layers are placed in freeze conditions.…”

Section: Transfer Learning and Fine Tuningmentioning

confidence: 99%

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

2021

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Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions.

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“…Despite considerable advances in computer vision, object detection is still an active topic of study [1][2][3][4]. This process is used in many fields, such as biomedical imaging, biometry, video surveillance, vehicle navigation, visual inspection, robot navigation, and remote sensing [1][2][3][4][5], to mention a few. Object identification has been considered an essential task and one of the biggest challenges in image processing [1][2][3][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…This process is used in many fields, such as biomedical imaging, biometry, video surveillance, vehicle navigation, visual inspection, robot navigation, and remote sensing [1][2][3][4][5], to mention a few. Object identification has been considered an essential task and one of the biggest challenges in image processing [1][2][3][6][7][8]. Several object recognition problems are solved utilizing digital image processing techniques, where segmentation methods are essential procedures [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

et al. 2021

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Object segmentation is a widely studied topic in digital image processing, as to it can be used for countless applications in several fields. This process is traditionally achieved by computing an optimal threshold from the image intensity histogram. Several algorithms have been proposed to find this threshold based on different statistical principles. However, the results generated via these algorithms contradict one another due to the many variables that can disturb an image. An accepted strategy to achieve the optimal histogram threshold, to distinguish between the object and the background, is to estimate two data distributions and find their intersection. This work proposes a strategy based on the Cuckoo Search Algorithm (CSA) and the Generalized Gaussian (GG) distribution to assess the optimal threshold. To test this methodology, we carried out several experiments in synthetic and practical scenarios and compared our results against other well-known algorithms from the literature. These practical cases comprise a medical image database and our own generated database. The results in a simulated environment show an evident advantage of the proposed strategy against other algorithms. In a real environment, this ranks among the best algorithms, making it a reliable alternative.

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Saliency guided faster-RCNN (SGFr-RCNN) model for object detection and recognition

Cited by 40 publications

References 15 publications

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Automated object detection of mechanical fasteners using faster region based convolutional neural networks

Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions

Alternative Thresholding Technique for Image Segmentation Based on Cuckoo Search and Generalized Gaussians

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