A convolutional neural network accelerator for real-time underwater image recognition of autonomous underwater vehicle

2020 ◽  
pp. 095965182095820
Author(s):  
Wanting Zhao ◽  
Hong Qi ◽  
Yu Jiang ◽  
Chong Wang ◽  
Fenglin Wei
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
High Performance ◽  
Autonomous Underwater Vehicle ◽  
Random Access ◽  
Underwater Vehicle ◽  
Underwater Image

In the field of underwater image recognition, a chip with smaller footprint and lower energy consumption is required to be implanted into autonomous intelligent underwater vehicle to make real-time response to the surrounding objects. Therefore, a promising accelerator with high performance and low energy consumption is designed, which optimizes the features possessed by convolutional neural network. The sharing of weights between neurons reduces the memory requirement. With all convolutional neural network data stored within on-chip static random-access memory, the need for memory access is drastically decreased. Besides, several small processing elements are used to form neural functional unit, which considerably reduces the bandwidth requirement through inter-processing element data transmission. By sending control signals to autonomous underwater vehicle, this accelerator enables it to avoid dangerous areas such as rocks and algae in time. The result suggests the proposed accelerator successfully achieves a higher processing speed than that of CPU and GPU with a footprint of 6.09 mm2 only and the energy consumption of 327.3 mW at 1 GHz.

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 Autonomous Underwater Vehicle Navigation Using Sonar Image Matching based on Convolutional Neural Network

2019 ◽  
Vol 52 (21) ◽  
pp. 156-162
Author(s):  
Wenli Yang ◽  
Shuangshuang Fan ◽  
Shuxiang Xu ◽  
Peter King ◽  
Byeong Kang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Matching ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Vehicle Navigation ◽  
Sonar Image

scholarly journals Fast recognition method of moving video images based on BP neural networks

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1024-1032 ◽  
Author(s):  
Yu Shao ◽  
Deden Witarsyah
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Consumption ◽  
Fault Tolerance ◽  
Real Time ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Image Features ◽  
Video Image ◽  
Error Signal

Abstract At present, the accuracy of real-time moving video image recognition methods are poor. Also energy consumption is high and fault tolerance is not ideal. Consequently this paper proposes a method of moving video image recognition based on BP neural networks. The moving video image is divided into two parts: the key content and the background by binary gray image. By collecting training cubes. The D-SFA algorithm is used to extract moving video image features and to construct feature representation. The image features are extracted by collecting training cubes. The BP neural network is constructed to get the error function. The error signal is returned continuously along the original path. By modifying the weights of neurons in each layer, the weights propagate to the input layer step by step, and then propagates forward. The two processes are repeated to minimize the error signal. The result of image feature extraction is regarded as the input of BP neural network, and the result of moving video image recognition is output. And fault tolerance in real-time is better than the current method. Also the recognition energy consumption is low, and our method is more practical.

scholarly journals A joint framework for underwater sequence images stitching based on deep neural network convolutional neural network

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091506 ◽  
Author(s):  
Mingwei Sheng ◽  
Songqi Tang ◽  
Zhuang Cui ◽  
Wanqi Wu ◽  
Lei Wan
Keyword(s):  
Neural Network ◽  
Image Registration ◽  
Convolutional Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Consensus Method ◽  
Feature Points ◽  
Underwater Imaging ◽  
Detection Range ◽  
Underwater Image ◽  
Symmetric Convolution

Panoramic stitching technology provides an effective solution for expanding visual detection range of the autonomous underwater vehicle. However, absorption and scattering of light in the water seriously deteriorate the underwater imaging in terms of distance and quality, especially the scattering sharply decreases the underwater image contrast and results in serious blur. This reduces the number of matching feature points between the underwater images to be stitched, while fewer matched points generated make image registration and stitching difficult. To solve the problem, a joint framework is established, which firstly involves a convolutional neural network-like algorithm composed of a symmetric convolution and deconvolution framework for underwater image enhancement. Then, it proposes an improved convolutional neural network-random sample consensus method based on VGGNet-16 framework to generate more correct matching feature points for image registration. The fusion method based on Laplacian pyramid is applied to eliminate artificial stitching traces and correct the position of stitching seam. Experimental results indicate that the proposed framework can restore the color and detail information of underwater images and generate more effective and sufficient matching feature points for underwater sequence images stitching.

scholarly journals Real-Time Underwater Image Recognition with FPGA Embedded System for Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 350 ◽  
Author(s):  
Minghao Zhao ◽  
Chengquan Hu ◽  
Fenglin Wei ◽  
Kai Wang ◽  
Chong Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Embedded System ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Image Recognition ◽  
Image Data ◽  
Recognition System ◽  
Frame Rate ◽  
High Definition ◽  
High Flexibility

The underwater environment is still unknown for humans, so the high definition camera is an important tool for data acquisition at short distances underwater. Due to insufficient power, the image data collected by underwater submersible devices cannot be analyzed in real time. Based on the characteristics of Field-Programmable Gate Array (FPGA), low power consumption, strong computing capability, and high flexibility, we design an embedded FPGA image recognition system on Convolutional Neural Network (CNN). By using two technologies of FPGA, parallelism and pipeline, the parallelization of multi-depth convolution operations is realized. In the experimental phase, we collect and segment the images from underwater video recorded by the submersible. Next, we join the tags with the images to build the training set. The test results show that the proposed FPGA system achieves the same accuracy as the workstation, and we get a frame rate at 25 FPS with the resolution of 1920 × 1080. This meets our needs for underwater identification tasks.

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