scholarly journals Image Recognition Technology in Texture Identification of Marine Sediment Sonar Image

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chao Sun ◽  
Li Wang ◽  
Nan Wang ◽  
Shaohua Jin
Keyword(s):  
Transfer Learning ◽  
Image Recognition ◽  
Marine Sediment ◽  
Original Model ◽  
Experimental Comparison ◽  
Texture Discrimination ◽  
Model Based ◽  
Sonar Image ◽  
Ocean Sediment ◽  
Sonar Images

Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP (average precision) value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images.

A field-programmable gate array system for sonar image recognition based on convolutional neural network

2020 ◽  
pp. 095965182093934
Author(s):  
Chong Wang ◽  
Yu Jiang ◽  
Kai Wang ◽  
Fenglin Wei
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Field Programmable Gate Array ◽  
Oil And Gas ◽  
Sonar Image ◽  
Field Programmable ◽  
Sonar Images ◽  
Gate Array

Subsea pipeline is the safest, most reliable, and most economical way to transport oil and gas from an offshore platform to an onshore terminal. However, the pipelines may rupture under the harsh working environment, causing oil and gas leakage. This calls for a proper device and method to detect the state of subsea pipelines in a timely and precise manner. The autonomous underwater vehicle carrying side-scan sonar offers a desirable way for target detection in the complex environment under the sea. As a result, this article combines the field-programmable gate array, featuring high throughput, low energy consumption and a high degree of parallelism, and the convolutional neural network into a sonar image recognition system. First, a training set was constructed by screening and splitting the sonar images collected by sensors, and labeled one by one. Next, the convolutional neural network model was trained by the set on the workstation platform. The trained model was integrated into the field-programmable gate array system and applied to recognize actual datasets. The recognition results were compared with those of the workstation platform. The comparison shows that the computational precision of the designed field-programmable gate array system based on convolutional neural network is equivalent to that of the workstation platform; however, the recognition time of the designed system can be saved by more than 77%, and its energy consumption can also be saved by more than 96.67%. Therefore, our system basically satisfies our demand for energy-efficient, real-time, and accurate recognition of sonar images.

scholarly journals Sonar image recognition of underwater target based on convolutional neural network

2021 ◽  
Vol 39 (2) ◽  
pp. 285-291
Author(s):  
Leilei Jin ◽  
Hong LIANG ◽  
Changsheng Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Recognition ◽  
Target Recognition ◽  
Saliency Detection ◽  
Image Features ◽  
Feature Maps ◽  
Sonar Image ◽  
Sonar Images ◽  
Underwater Target

Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.

scholarly journals Shipwreck Target Recognition in Side-Scan Sonar Images by Improved YOLOv3 Model Based on Transfer Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 173450-173460
Author(s):  
Tang Yulin ◽  
Shaohua Jin ◽  
Gang Bian ◽  
Yonghou Zhang
Keyword(s):  
Transfer Learning ◽  
Target Recognition ◽  
Side Scan Sonar ◽  
Model Based ◽  
Sonar Images

scholarly journals A standardization model based on image recognition for performance evaluation of an oral scanner

2017 ◽  
Vol 9 (6) ◽  
pp. 409 ◽  
Author(s):  
Sang-Wan Seo ◽  
Wan-Sun Lee ◽  
Jae-Young Byun ◽  
Kyu-Bok Lee
Keyword(s):  
Performance Evaluation ◽  
Image Recognition ◽  
Model Based

scholarly journals Performance of Cans Classification System for Different Conveyor Belt Speed using Naïve Bayes

2020 ◽  
Vol 5 (4) ◽  
pp. 111
Author(s):  
Yulia Resti ◽  
Firmansyah Burlian ◽  
Irsyadi Yani
Keyword(s):  
Classification System ◽  
Naive Bayes ◽  
Conveyor Belt ◽  
Original Model ◽  
Model Based ◽  
Accuracy Level ◽  
Conveyor Belts ◽  
Sorting Process ◽  
Input Variables

The classification system in the sorting process in the can recycling industry can be made based on digital images by exploring the basic color pixel values ​​of images such as R, G, and B as variable inputs. In real time, the classification of cans in the sorting process occurs when cans placed on a conveyor belt move at a certain speed. This paper discusses the performance of can classification systems using the Naïve Bayes method. This method can handle all types of variables, including when all variables are continuous. Two types of conveyor belts are designed to get different speeds, and all images of the cans are captured on both conveyor belts. Two models of Bayes naive are built on the basis of the different distribution assumptions; the original model (all Gaussian distributed) and the model based on the best distribution. Performance of the classification system is built by dividing data into the learning data and the testing data with a composition of 50:50 in which each data is designed into 50 groups with different percentages on each type of cans using sampling technique without replacement. The results obtained are, first, the speed of the conveyor belt when capturing an image affects the pixel values of red, green, and blue and ultimately affects the results of the classification of cans. Second, not all input variables are Gaussian distributed. The classification system was built using assumption the best distribution model for each input variable has the better average accuracy level than the model that assumes all input variables are Gaussian distributed, and the accuracy level of classification on the first speeds of conveyor belt with a gear ratio of 12:30 and a diameter of 35 mm has an accuracy that is better than the other speed, both on the original model and the model based on the best distribution. However, it is necessary to test more statistical distribution models to obtain significant results.

Underwater Sonar Image Segmentation Based on Snake Model

2013 ◽  
Vol 448-453 ◽  
pp. 3675-3678
Author(s):  
Jun Peng Wu ◽  
Hai Tao Guo
Keyword(s):  
Image Segmentation ◽  
Greedy Algorithm ◽  
Echo Signal ◽  
Constraint Forces ◽  
Shape Accuracy ◽  
Snake Model ◽  
Sonar Image ◽  
Sonar Images ◽  
Underwater Target ◽  
Detection And Recognition

The underwater sonar image segmentation has been a topic of research for decades. Underwater sonar image is based on the interaction by the echo signal of sound toward the underwater objects or targets. Because of the serious noises polution and the dim target edge, the contrast and resolution of sonar images are obtaind in a decreased quanlity. This paper proposes an improved snake model that focuses on solving underwater target detection and recognition. According to the traditional snake model, it is defined as an energy minimizing spline which is influenced by external constraint forces, and it can guide the image forces to pull toward features, such as lines or edges. Compared with the traditional snake model, this snake model greedy algorithm can converge to the contours more quickly and more stably, especially in complex underwater environments. Examination of the results shows that using snake model greedy algorithm has a more clear shape accuracy.

Sign in / Sign up

Export Citation Format

Share Document