scholarly journals А Nоvel Data-Driven Орtimаl Methоdоlоgy fоr Deteсting Shiр from Sаr Images Bаsed on Аrtifiсiаl Intelligenсe

2021 ◽  
Vol 1 (3) ◽  
pp. 17-22
Author(s):  
M.S.Antony Vigil ◽  
Rishabh Jain ◽  
Tanmay Agarwal ◽  
Abhinav Chandra
Keyword(s):  
Target Detection ◽  
Detection Rate ◽  
Target Identification ◽  
Detection Algorithm ◽  
High Accuracy ◽  
Data Driven ◽  
Identification Rate ◽  
Ship Detection ◽  
Hyper Spectral ◽  
Multiple Issues

There are a variety of deep learning algorithms available in the supervision of ships, but they are dealing with multiple issues of inaccurate identification rate and inadequate target detection speed. At this stage, an algorithm is given оn Соnvоlutiоnаl Neural Network for target identification and detection using the ship image. The study involves the investigation of the reactions of hyper spectral atmospheric rectification on the accurate and precise results of ship detection. The ship features which were detected from two atmospheric rectified algorithms on airborne hyper spectral data were corrected by the application of these algorithms with the help of an unsupervised target detection procedure. High accuracy and fast ship identification was a result of this algorithm and using unique modules, improving the loss function and enlargement of data for the smaller targets. The results of the experiments show that our algorithm has given much better detection rate as compared to target detection algorithm using traditional machine learning.

scholarly journals Dim-Small Target Detection Based on Adaptive Pipeline Filtering

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Biao Li ◽  
Xu Zhiyong ◽  
Jianlin Zhang ◽  
Xiangru Wang ◽  
Xiangsuo Fan
Keyword(s):  
Target Detection ◽  
Time Domain ◽  
Detection Rate ◽  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Background Suppression ◽  
Signal To Noise ◽  
Filtering Algorithm ◽  
Small Target Detection ◽  
Smooth Motion

In order to improve the robustness of the pipeline target detection algorithm against strong noises and occlusion, this paper presents an adaptive pipeline filtering algorithm (APFA). In APFA, the velocity and the center of the target are firstly predicted based on the smooth motion trajectory after background suppression. Then, time-domain energy enhancement of targets is adopted to improve the obscure target detection, and adaptively updating the center and radius of the pipeline filter are carried out for targets’ motion variation. Experiments on five different typical scenes show that APFA can improve the robustness of the pipeline filter against strong noises and even when targets are temporarily obscured partially or completely. Simultaneously, APFA can significantly improve the energy and signal-to-noise ratio of targets, and as a result, the target detection rate is significantly promoted on all experiments.

Target Detection of Sonar Image Based on Bis-Parameter with Adaptive Windows

2013 ◽  
Vol 347-350 ◽  
pp. 3407-3410
Author(s):  
Ke Li ◽  
Zhong Liu ◽  
Sheng Liang Hu ◽  
Yang Liu
Keyword(s):  
Target Detection ◽  
Detection Algorithm ◽  
High Accuracy ◽  
New Method ◽  
Experimental Result ◽  
Image Process ◽  
Sar Image ◽  
Sonar Image

The detection algorithm CFAR is very mature in SAR image process field and the efficiency is very good. In the paper, CFAR is tried to be used in sonar image process. In order to solve the problem that the target part leaking to the background, a new method target detection of sonar image based on bis-parameter with adaptive windows is proposed. The size of adaptive windows can be adjusted to totally cover different targets. The experimental result showed that the complex multi target can be detected by the proposed method in a high accuracy.

A novel infrared moving target detection algorithm based on multiscale codebook model

2016 ◽  
Vol 14 (04) ◽  
pp. 1650023
Author(s):  
Lei Liu ◽  
Yayun Zhou ◽  
He Li ◽  
Wei Huang ◽  
Minjie Cui
Keyword(s):  
Target Detection ◽  
Detection Rate ◽  
Subjective Evaluation ◽  
Objective Evaluation ◽  
Detection Algorithm ◽  
Moving Target ◽  
False Detection Rate ◽  
False Detection ◽  
Moving Target Detection ◽  
Fusion Operation

Traditional target detection algorithm based on codebook model only use pixel information of video image while spatial scale information of image is ignored, so the detection result usually has high false detection rate and the target’s characteristics is not obvious. To overcome this difficulty, a novel infrared (IR) moving target detection algorithm based on multiscale codebook model is presented in this paper. The main principle of this algorithm is to make full use of image pixel information and scale information for moving target detection. First, by Gauss pyramid image hierarchical model, the IR video is stratified into three layers, namely the original layer, the second layer and the top layer. Second, background codebook model is built for each layer image, the main feature information is discovered to update background codebook models, and then moving target in IR video is detected according to the updated background model. Finally, the fusion operation is done on detection results of three layers to get the final detection result. Compared with traditional detection algorithm based on codebook model, this IR target detection algorithm combines image pixel information and scale characteristics. By using this novel algorithm, the experiments on some real world IR images are performed. The whole algorithm implementing processes and results are analyzed, and this novel detection algorithm is evaluated from the two aspects: subjective evaluation and objective evaluation. From the experiment results, we can see that the proposed method has better detection effects, richer target information and lower false detection rate.

scholarly journals Classification of Radar Environment Using Ensemble Neural Network with Variation of Hidden Neuron Number

2017 ◽  
Vol 17 (1) ◽  
pp. 19 ◽  
Author(s):  
Budiman Putra Asmaur Rohman ◽  
Dayat Kurniawan
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Detection Rate ◽  
Detection Algorithm ◽  
Ensemble Classifier ◽  
Radar System ◽  
Constant False Alarm Rate ◽  
Neuron Number ◽  
Radar Systems ◽  
Ensemble Neural Network

Target detection is a mandatory task of radar system so that the radar system performance is mainly determined by its detection rate. Constant False Alarm Rate (CFAR) is a detection algorithm commonly used in radar systems. This method is divided into several approaches which have different performance in the different environments. Therefore, this paper proposes an ensemble neural network based classifier with a variation of hidden neuron number for classifying the radar environments. The result of this research will support the improvement of the performance of the target detection on the radar systems by developing such an adaptive CFAR. Multi-layer perceptron network (MLPN) with a single hidden layer is employed as the structure of base classifiers. The first step of this research is the evaluation of the hidden neuron number giving the highest accuracy of classification and the simplicity of computation. According to the result of this step, the three best structures are selected to build an ensemble classifier. On the ensemble structure, all of those three MLPN outputs then be collected and voted for getting the majority result in order to decide the final classification. The three possible radar environments investigated are homogeneous, multiple-targets and clutter boundary. According to the simulation results, the ensemble MLPN provides a higher detection rate than the conventional single MLPNs. Moreover, in the multiple-target and clutter boundary environments, the proposed method is able to show its highest performance.

scholarly journals Sea–Sky Line and its Nearby Ships Detection Based on the Motion Attitude of Visible Light Sensors

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 4004 ◽  
Author(s):  
Shan ◽  
Zhao ◽  
Pan ◽  
Wang ◽  
Zhao
Keyword(s):  
Visible Light ◽  
Target Detection ◽  
Degrees Of Freedom ◽  
Saliency Detection ◽  
Detection Algorithm ◽  
Visual Development ◽  
Sensor Data ◽  
Camera Motion ◽  
Six Degrees Of Freedom ◽  
Ship Detection

In the maritime scene, visible light sensors installed on ships have difficulty accurately detecting the sea–sky line (SSL) and its nearby ships due to complex environments and six-degrees-of-freedom movement. Aimed at this problem, this paper combines the camera and inertial sensor data, and proposes a novel maritime target detection algorithm based on camera motion attitude. The algorithm mainly includes three steps, namely, SSL estimation, SSL detection, and target saliency detection. Firstly, we constructed the camera motion attitude model by analyzing the camera's six-degrees-of-freedom motion at sea, estimated the candidate region (CR) of the SSL, then applied the improved edge detection algorithm and the straight-line fitting algorithm to extract the optimal SSL in the CR. Finally, in the region of ship detection (ROSD), an improved visual saliency detection algorithm was applied to extract the target ships. In the experiment, we constructed SSL and its nearby ship detection dataset that matches the camera’s motion attitude data by real ship shooting, and verified the effectiveness of each model in the algorithm through comparative experiments. Experimental results show that compared with the other maritime target detection algorithm, the proposed algorithm achieves a higher detection accuracy in the detection of the SSL and its nearby ships, and provides reliable technical support for the visual development of unmanned ships.

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