Solar Event Tracking with Deep Regression Networks: A Proof of Concept Evaluation

Sarker, Toqi Tahamid; Banda, Juan M.

doi:10.1109/bigdata47090.2019.9006273

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…The regression CNN is specialized to solving regression problems [21][22][23][24]. It is applicable to address numerical regression forecasting questions.…”

Section: Key Cnn Architecturementioning

confidence: 99%

Tilt Angle Detection and Correction of License Plate Based on Convolutional Neural Network

2022

IJOMAM

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In the intelligent traffic field, accurate recognition of license plate information is not only conducive to the handling of traffic accidents, but also beneficial to safety in the autonomous field. However, the identification of license plate information becomes very difficult if there are tilted angles in the license plate image. The objective of the work is to establish a convolutional neural network (CNN)-based license plate tilt angle recognition network realizing the horizontal and vertical tilt angles detection of the license plate accurately; and then, according to the detected angles, the tilt angle detection algorithm is proposed to realize the tilted license plate image correction and character segmentation. Firstly, the YOLOv3 is adopted to locate license plate and classify the clockwise rotation and anticlockwise rotation license plate images. Secondly, the regression CNN is established to detect the horizontal and vertical tilt angle of the cropped license plate image. Thirdly, the tilted license plate correction algorithm is proposed to correct the horizontal and vertical tilt angles of the license plate using image rotation and shearing transformation theory according to the detected horizontal and vertical tilt angles. Subsequently, the bilinear interpolation is used to improve the correction performance. Finally, the characters of the license plate are segmented from the corrected license plate image after removing the upper and lower boundary. The results show that the proposed tilt angle detection network and the correction algorithm have a good performance, which is superior to the current state-of-the-art tilted license plate correction algorithm.

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“…The regression CNN is specialized to solving regression problems [21][22][23][24]. It is applicable to address numerical regression forecasting questions.…”

Section: Key Cnn Architecturementioning

confidence: 99%

Tilt Angle Detection and Correction of License Plate Based on Convolutional Neural Network

2022

IJOMAM

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“…A lot of new trackers have successfully manipulated the dynamic representations obtained by CNN. Among the earlier studies that use CNN are GOTURN [11] and SiamFC [12], which employ shallow networks and execute in real time platforms. GOTURN employed CNN for extracting features from the targeted and searching regions.…”

Section: Introductionmentioning

confidence: 99%

Robust Deep Transfer Learning Based Object Detection and Tracking Approach

Narmadha¹,

Kavitha²,

Poonguzhali³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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At present days, object detection and tracking concepts have gained more importance among researchers and business people. Presently, deep learning (DL) approaches have been used for object tracking as it increases the performance and speed of the tracking process. This paper presents a novel robust DL based object detection and tracking algorithm using Automated Image Annotation with ResNet based Faster regional convolutional neural network (R-CNN) named (AIA-FRCNN) model. The AIA-RFRCNN method performs image annotation using a Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR) called DCF-CSRT model. The AIA-RFRCNN model makes use of Faster RCNN as an object detector and tracker, which involves region proposal network (RPN) and Fast R-CNN. The RPN is a full convolution network that concurrently predicts the bounding box and score of different objects. The RPN is a trained model used for the generation of the high-quality region proposals, which are utilized by Fast R-CNN for detection process. Besides, Residual Network (ResNet 101) model is used as a shared convolutional neural network (CNN) for the generation of feature maps. The performance of the ResNet 101 model is further improved by the use of Adam optimizer, which tunes the hyperparameters namely learning rate, batch size, momentum, and weight decay. Finally, softmax layer is applied to classify the images. The performance of the AIA-RFRCNN method has been assessed using a benchmark dataset and a detailed comparative analysis of the results takes place. The outcome of the experiments indicated the superior characteristics of the AIA-RFRCNN model under diverse aspects.

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