Sonar image recognition based on fine-tuned convolutional neural network

To solve the problem of sonar image recognition, a sonar image recognition method based on fine-tuned Convolutional Neural Network (CNN) is proposed in this paper. With the development of deep learning, CNN shows impressive performance in image recognition. However, massive data is needed to train a CNN from beginning. Through fine-tuning pre-trained CNN can help us training CNN from relatively high starting points, based on those pre-trained CNNs, only few data is needed to retrain a CNN which focus on sonar image recognition. A scaled model experiment shows that based on the architecture of AlexNet, compared with the traditional learning method, the transfer learning method can achieve higher recognition accurate rate of 95.81% and less training time. Moreover, this paper also compared 6 pre-trained networks, among those networks, VGG16 can achieve the highest recognition rate of 99.48%.

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

Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering ◽

10.1177/0959651820939345 ◽

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.

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Small aircraft detection using deep learning

Aircraft Engineering and Aerospace Technology ◽

10.1108/aeat-11-2020-0259 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Emre Kiyak ◽

Gulay Unal

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Fine Tuning ◽

Deep Convolutional Neural Networks ◽

Content Type ◽

Training Time

Purpose The paper aims to address the tracking algorithm based on deep learning and four deep learning tracking models developed. They compared with each other to prevent collision and to obtain target tracking in autonomous aircraft. Design/methodology/approach First, to follow the visual target, the detection methods were used and then the tracking methods were examined. Here, four models (deep convolutional neural networks (DCNN), deep convolutional neural networks with fine-tuning (DCNNFN), transfer learning with deep convolutional neural network (TLDCNN) and fine-tuning deep convolutional neural network with transfer learning (FNDCNNTL)) were developed. Findings The training time of DCNN took 9 min 33 s, while the accuracy percentage was calculated as 84%. In DCNNFN, the training time of the network was calculated as 4 min 26 s and the accuracy percentage was 91%. The training of TLDCNN) took 34 min and 49 s and the accuracy percentage was calculated as 95%. With FNDCNNTL, the training time of the network was calculated as 34 min 33 s and the accuracy percentage was nearly 100%. Originality/value Compared to the results in the literature ranging from 89.4% to 95.6%, using FNDCNNTL, better results were found in the paper.

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Sonar image recognition of underwater target based on convolutional neural network

Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University ◽

10.1051/jnwpu/20213920285 ◽

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.

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Chip Appearance Defect Recognition Based on Convolutional Neural Network

Sensors ◽

10.3390/s21217076 ◽

2021 ◽

Vol 21 (21) ◽

pp. 7076

Author(s):

Jun Wang ◽

Xiaomeng Zhou ◽

Jingjing Wu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Region Of Interest ◽

Classification Model ◽

Defect Classification ◽

Accuracy Rate ◽

Automatic Data ◽

Training Time ◽

Defect Recognition

To improve the recognition rate of chip appearance defects, an algorithm based on a convolution neural network is proposed to identify chip appearance defects of various shapes and features. Furthermore, to address the problems of long training time and low accuracy caused by redundant input samples, an automatic data sample cleaning algorithm based on prior knowledge is proposed to reduce training and classification time, as well as improve the recognition rate. First, defect positions are determined by performing image processing and region-of-interest extraction. Subsequently, interference samples between chip defects are analyzed for data cleaning. Finally, a chip appearance defect classification model based on a convolutional neural network is constructed. The experimental results show that the recognition miss detection rate of this algorithm is zero, and the accuracy rate exceeds 99.5%, thereby fulfilling industry requirements.

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Forest fire image recognition based on convolutional neural network

Journal of Algorithms & Computational Technology ◽

10.1177/1748302619887689 ◽

2019 ◽

Vol 13 ◽

pp. 174830261988768 ◽

Cited By ~ 3

Author(s):

Yuanbin Wang ◽

Langfei Dang ◽

Jieying Ren

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Processing ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Forest Fire ◽

Recognition Rate ◽

Extraction Process ◽

Fire Flame

In order to detect fire automatically, a forest fire image recognition method based on convolutional neural networks is proposed in this paper. There are two main types of fire recognition algorithms. One is based on traditional image processing technology and the other is based on convolutional neural network technology. The former is easy to lead in false detection because of blindness and randomness in the stage of feature selection, while for the latter the unprocessed convolutional neural network is applied directly, so that the characteristics learned by the network are not accurate enough, and recognition rate may be affected. In view of these problems, conventional image processing techniques and convolutional neural networks are combined, and an adaptive pooling approach is introduced. The fire flame area can be segmented and the characteristics can be learned by this algorithm ahead. At the same time, the blindness in the traditional feature extraction process is avoided, and the learning of invalid features in the convolutional neural network is also avoided. Experiments show that the convolutional neural network method based on adaptive pooling method has better performance and has higher recognition rate.

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Dual-channel convolutional neural network for power edge image recognition

Journal of Cloud Computing Advances Systems and Applications ◽

10.1186/s13677-021-00235-9 ◽

2021 ◽

Vol 10 (1) ◽

Author(s):

Fangrong Zhou ◽

Yi Ma ◽

Bo Wang ◽

Gang Lin

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Recognition ◽

Recognition Rate ◽

Recognition Algorithm ◽

Classification Method ◽

Power Equipment ◽

Recognition Method ◽

Edge Image ◽

Dual Channel

AbstractIn view of the low accuracy and poor processing capacity of traditional power equipment image recognition methods, this paper proposes a power equipment image recognition method based on a dual-channel convolutional neural network (DC-CNN) model and random forest (RF) classification. In the aspect of feature extraction, the DC-CNN model extracts the characteristics of power equipment through two independent CNN models. In the aspect of the recognition algorithm, by referring to the advantages of the traditional machine learning method and incorporating the advantages of the RF, an RF classification method incorporating deep learning is proposed. Finally, the proposed DC-CNN model and RF classification method are used to classify images of various types of power equipment. The results show that the proposed methods can be effectively applied to the image recognition of various types of power equipment, and they greatly improve the recognition rate of power equipment images.

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Recognition of 3D Shapes Based on 3V-DepthPano CNN

Mathematical Problems in Engineering ◽

10.1155/2020/7584576 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 1

Author(s):

Junjie Yin ◽

Ningning Huang ◽

Jing Tang ◽

Meie Fang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Shape Descriptor ◽

Recognition System ◽

Fine Tuning ◽

Training Time ◽

3D Shape ◽

3D Shapes ◽

And Training

This paper proposes a convolutional neural network (CNN) with three branches based on the three-view drawing principle and depth panorama for 3D shape recognition. The three-view drawing principle provides three key views of a 3D shape. A depth panorama contains the complete 2.5D information of each view. 3V-DepthPano CNN is a CNN system with three branches designed for depth panoramas generated from the three key views. This recognition system, i.e., 3V-DepthPano CNN, applies a three-branch convolutional neural network to aggregate the 3D shape depth panorama information into a more compact 3D shape descriptor to implement the classification of 3D shapes. Furthermore, we adopt a fine-tuning technique on 3V-DepthPano CNN and extract shape features to facilitate the retrieval of 3D shapes. The proposed method implements a good tradeoff state between higher accuracy and training time. Experiments show that the proposed 3V-DepthPano CNN with 3 views obtains approximate accuracy to MVCNN with 12/80 views. But the 3V-DepthPano CNN frame takes much shorter time to obtain depth panoramas and train the network than MVCNN. It is superior to all other existing advanced methods for both classification and shape retrieval.

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Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks

Electronics ◽

10.3390/electronics10151823 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1823

Author(s):

Qiang Ge ◽

Fengxue Ruan ◽

Baojun Qiao ◽

Qian Zhang ◽

Xianyu Zuo ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Object Classification ◽

Fine Tuning ◽

Visual Fatigue ◽

Style Transfer ◽

Side Scan Sonar ◽

Sonar Image ◽

Sonar Images

Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar image classification method based on synthetic data and transfer learning is proposed in this paper. In this method, optical images are used as inputs and the style transfer network is employed to simulate the side-scan sonar image to generate “simulated side-scan sonar images”; meanwhile, a convolutional neural network pre-trained on ImageNet is introduced for classification. In this paper, we experimentally demonstrate that the maximum accuracy of target classification is up to 97.32% by fine-tuning the pre-trained convolutional neural network using a training set incorporating “simulated side-scan sonar images”. The results show that the classification accuracy can be effectively improved by combining a pre-trained convolutional neural network and “similar side-scan sonar images”.

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