An Adaptive Detection Algorithm for Small Targets in Digital Image

2015 ◽  
pp. 333-339
Author(s):  
Shumei Wang
Keyword(s):  
Digital Image ◽  
Detection Algorithm ◽  
Adaptive Detection ◽  
Small Targets

scholarly journals Pedestrian detection algorithm based on improved muti-scale feature fusion

2021 ◽  
Vol 2078 (1) ◽  
pp. 012008
Author(s):  
Hui Liu ◽  
Keyang Cheng
Keyword(s):  
Clustering Algorithm ◽  
Feature Fusion ◽  
Pedestrian Detection ◽  
Detection Algorithm ◽  
Data Sets ◽  
False Detection ◽  
Scale Feature ◽  
Multi Scale ◽  
Dilated Convolution ◽  
Small Targets

Abstract Aiming at the problem of false detection and missed detection of small targets and occluded targets in the process of pedestrian detection, a pedestrian detection algorithm based on improved multi-scale feature fusion is proposed. First, for the YOLOv4 multi-scale feature fusion module PANet, which does not consider the interaction relationship between scales, PANet is improved to reduce the semantic gap between scales, and the attention mechanism is introduced to learn the importance of different layers to strengthen feature fusion; then, dilated convolution is introduced. Dilated convolution reduces the problem of information loss during the downsampling process; finally, the K-means clustering algorithm is used to redesign the anchor box and modify the loss function to detect a single category. The experimental results show that the improved pedestrian detection algorithm in the INRIA and WiderPerson data sets under different congestion conditions, the AP reaches 96.83% and 59.67%, respectively. Compared with the pedestrian detection results of the YOLOv4 model, the algorithm improves by 2.41% and 1.03%, respectively. The problem of false detection and missed detection of small targets and occlusion has been significantly improved.

Locally adaptive detection algorithm for forward-looking ground-penetrating radar

2010 ◽  
Author(s):  
Timothy C. Havens ◽  
K. C. Ho ◽  
Justin Farrell ◽  
James M. Keller ◽  
Mihail Popescu ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Detection Algorithm ◽  
Adaptive Detection ◽  
Locally Adaptive ◽  
Ground Penetrating ◽  
Forward Looking

INFRARED SMALL TARGET DETECTION ALGORITHM BASED ON FEATURE SALIENCE AND MULTI-FEATURES FUSION

2011 ◽  
Vol 25 (02) ◽  
pp. 299-308 ◽  
Author(s):  
ZHEN-XUE CHEN ◽  
CHENG-YUN LIU ◽  
FA-LIANG CHANG
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Absolute Difference ◽  
Small Target ◽  
Probability Of Error ◽  
Small Target Detection ◽  
Low Snr ◽  
Complex Background ◽  
Small Targets

It is an important and challenging problem to detect small targets in clutter scene and low SNR (Signal Noise Ratio) in infrared (IR) images. In order to solve this problem, a method based on feature salience is proposed for automatic detection of targets in complex background. Firstly, in this paper, the method utilizes the average absolute difference maximum (AADM) as the dissimilarity measurement between targets and background region to enhance targets. Secondly, minimum probability of error was used to build the model of feature salience. Finally, by computing the realistic degree of features, this method solves the problem of multi-feather fusion. Experimental results show that the algorithm proposed shows better performance with respect to the probability of detection. It is an effective and valuable small target detection algorithm under a complex background.

Forest Fire Detection Algorithm Based on Digital Image

2013 ◽  
Vol 8 (8) ◽  
Author(s):  
Rui Chen ◽  
Yuanyuan Luo ◽  
Mohanmad Reza Alsharif
Keyword(s):  
Forest Fire ◽  
Digital Image ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Forest Fire Detection

scholarly journals Detection method based on improved faster R-CNN for pin defect in transmission lines

2021 ◽  
Vol 300 ◽  
pp. 01011
Author(s):  
Jun Wu ◽  
Sheng Cheng ◽  
Shangzhi Pan ◽  
Wei Xin ◽  
Liangjun Bai ◽  
...  
Keyword(s):  
Transmission Line ◽  
Defect Detection ◽  
Transmission Lines ◽  
False Positive Rate ◽  
Detection Algorithm ◽  
Detection Model ◽  
Positive Rate ◽  
Aerial Vehicle ◽  
The Stability ◽  
Small Targets

Defects such as insulator, pins, and counterweight in highvoltage transmission lines affect the stability of the power system. The small targets such as pins in the unmanned aerial vehicle (UAV) inspection images of transmission lines occupy a small proportion in the images and the characteristic representations are poor which results a low defect detection rate and a high false positive rate. This paper proposed a transmission line pin defect detection algorithm based on improved Faster R-CNN. First, the pre-training weights with higher matching degree are obtained based on transfer learning. And it is applied to construct defect detection model. Then, the regional proposal network is used to extract features in the model. The results of defect detection are obtained by regression calculation and classification of regional characteristics. The experimental results show that the accuracy of the pin defect detection of the transmission line reaches 81.25%

scholarly journals Dim and Small Targets Detection in Sequence Images Based on Spatiotemporal Motion Characteristics

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Fan Xiangsuo ◽  
Hongwei Guo ◽  
Xu Zhiyong ◽  
Biao Li
Keyword(s):  
Detection Algorithm ◽  
Spatial Domain ◽  
Mean Value ◽  
Local Motion ◽  
Detection Rates ◽  
Motion Characteristics ◽  
Small Targets ◽  
The Time Domain ◽  
Diffusion Function ◽  
Sequential Images

In order to effectively enhance the low detection rates of dim and small targets caused by dynamic backgrounds, this paper proposes a detection algorithm for dim and small targets in sequence images based on spatiotemporal motion characteristics. With regard to the spatial domain, this paper proposes an improved anisotropic background filtering algorithm that makes full use of the gradient differences between the target and the background pixels in the eight directions of the spatial domain and selects the mean value of the three directions with the lowest diffusion function in the eight directions as the differential filter to obtain a differential image. Then, the paper proposes a directional energy correlation enhancement algorithm in the time domain. Finally, based on the above preprocessing operations, we construct a dim and small targets detection algorithm in sequence images with local motion characteristics, which achieves target detection by determining the number of occurrences of the target, the number of displacements, and the total cumulative area in these sequential images. Experiments show that the proposed detection algorithm in this paper can effectively improve the detection of dim and small targets in dynamic scenes.

scholarly journals Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Xiangsuo Fan ◽  
Zhiyong Xu ◽  
Jianlin Zhang ◽  
Yongmei Huang ◽  
Zhenming Peng
Keyword(s):  
Detection Method ◽  
Detection Algorithm ◽  
Local Energy ◽  
Signal Noise ◽  
Sequential Image ◽  
Energy Center ◽  
Overall Performance ◽  
Noise Ratio ◽  
Motion Characteristics ◽  
Small Targets

In order to detect infrared (IR) dim and small targets in a strong clutter background, a method based on local energy center of sequential image is proposed. This paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HOC). Finally, on the basis of image preprocessing, the paper constructs a sequential image energy center detection algorithm that integrates the neighborhood, continuity, area, and energy and other motion characteristics of the target. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HOC significantly increased the signal-noise ratio of images; when the signal-noise ratio (SNR) is lower than 2.5 dB, the proposed method could effectively eliminate noise and detect targets.

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