Chessboard corner detection based on circular template

AbstractInfrared small target detection has been a challenging task due to the weak radiation intensity of targets and the complexity of the background. Traditional methods using hand-designed features are usually effective for specific background and have the problems of low detection rate and high false alarm rate in complex infrared scene. In order to fully exploit the features of infrared image, this paper proposes an infrared small target detection method based on region proposal and convolution neural network. Firstly, the small target intensity is enhanced according to the local intensity characteristics. Then, potential target regions are proposed by corner detection to ensure high detection rate of the method. Finally, the potential target regions are fed into the classifier based on convolutional neural network to eliminate the non-target regions, which can effectively suppress the complex background clutter. Extensive experiments demonstrate that the proposed method can effectively reduce the false alarm rate, and outperform other state-of-the-art methods in terms of subjective visual impression and quantitative evaluation metrics.

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Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

Applied Sciences ◽

10.3390/app11093863 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3863

Author(s):

Ali Emre Öztürk ◽

Ergun Erçelebi

Keyword(s):

Deep Learning ◽

Image Classification ◽

Synthetic Data ◽

Real Data ◽

Corner Detection ◽

Batch Size ◽

Test Accuracy ◽

Classification Problems ◽

Auc Value ◽

Classification Test

A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks.

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MiRNA Detection Using a Rolling Circle Amplification and RNA-Cutting Allosteric Deoxyribozyme Dual Signal Amplification Strategy

Biosensors ◽

10.3390/bios11070222 ◽

2021 ◽

Vol 11 (7) ◽

pp. 222

Author(s):

Chenxin Fang ◽

Ping Ouyang ◽

Yuxing Yang ◽

Yang Qing ◽

Jialun Han ◽

...

Keyword(s):

Signal Amplification ◽

Rolling Circle Amplification ◽

Padlock Probe ◽

Rolling Circle ◽

Sensing Platform ◽

Mirna Detection ◽

Substrate Cleavage ◽

Amplification Strategy ◽

Circular Template ◽

Dual Signal Amplification

A microRNA (miRNA) detection platform composed of a rolling circle amplification (RCA) system and an allosteric deoxyribozyme system is proposed, which can detect miRNA-21 rapidly and efficiently. Padlock probe hybridization with the target miRNA is achieved through complementary base pairing and the padlock probe forms a closed circular template under the action of ligase; this circular template results in RCA. In the presence of DNA polymerase, RCA proceeds and a long chain with numerous repeating units is formed. In the presence of single-stranded DNA (H1 and H2), multi-component nucleic acid enzymes (MNAzymes) are formed that have the ability to cleave substrates. Finally, substrates containing fluorescent and quenching groups and magnesium ions are added to the system to activate the MNAzyme and the substrate cleavage reaction, thus achieving fluorescence intensity amplification. The RCA–MNAzyme system has dual signal amplification and presents a sensing platform that demonstrates broad prospects in the analysis and detection of nucleic acids.

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