Infrared Small Target Detection Based on Fully Convolutional Neural Network and Visual Saliency

2020 ◽  
Vol 49 (7) ◽  
pp. 710003
Author(s):  
刘俊明 Jun-ming LIU ◽  
孟卫华 Wei-hua MENG
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Visual Saliency ◽  
Small Target ◽  
Small Target Detection ◽  
Infrared Small Target

scholarly journals Infrared small target detection based on region proposal and CNN classifier

2021 ◽  
Author(s):  
Mingming Fan ◽  
Shaoqing Tian ◽  
Kai Liu ◽  
Jiaxin Zhao ◽  
Yunsong Li
Keyword(s):  
Neural Network ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Rate ◽  
Corner Detection ◽  
Small Target ◽  
Small Target Detection ◽  
Potential Target ◽  
Infrared Small Target

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.

scholarly journals A Small Target Detection Research Based on Dynamic Convolution Neural Network

2021 ◽  
Author(s):  
Chenshuai Bai ◽  
Kaijun Wu ◽  
Dicong Wang ◽  
Hong Li ◽  
Mingjun Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Target Detection ◽  
Image Data ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Small Target ◽  
Training Process ◽  
Small Target Detection

Abstract Because the detection effect of EfficientNet-YOLOv3 target detection algorithm is not very good, this paper proposes a small target detection research based on dynamic convolution neural network. Firstly, the dynamic convolutional neural network is introduced to replace the traditional convolutional neural network, which makes the algorithm model more robust; Secondly, in the training process, the optimization parameters are continuously adjusted to further strengthen the model structure; Finally, in order to prevent over fitting, the Learning Rate and Batch Size parameters are modified during the training process. remote sensing image The results of the proposed algorithm on RSOD remote sensing image data sets show that compared with the original EfficientNet-YOLOv3 algorithm, the (Average Precision, AP) value is increased by 1.93% and the (Log Average Miss Rate ,LAMR) value is reduced by 0.0500; The results of the proposed algorithm on TGRS-HRRSD remote sensing image data set show that compared with the original EfficientNet-YOLOv3 algorithm, the mAP value is increased by 0.07% and the mLAMR value is reduced by 0.0007.

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