Abstract
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.
Highlights
IntroductionWe usually use sensors, such as sonar and cameras, to collect underwater object information [1,2,3]
The autonomous intelligent underwater vehicle (AUV) is an underwater robot, which has the advantages of a large range of activities, safety, and intelligence, and has become an important tool for the completion of various underwater tasks
We propose the Field-Programmable Gate Array (FPGA) chip with low power consumption to perform the real-time image recognition
Summary
We usually use sensors, such as sonar and cameras, to collect underwater object information [1,2,3]. The recognition degree of cameras is higher than that of sonar, but higher computational complexity is required in analyzing the whole data. The autonomous intelligent underwater vehicle (AUV) is an underwater robot, which has the advantages of a large range of activities, safety, and intelligence, and has become an important tool for the completion of various underwater tasks. It can be used for laying pipelines, undersea inspection, data collection, drilling support, submarine construction, maintenance, and repair of underwater equipment, etc. The AUV seldom processes or analyzes the images captured by the camera in real time, which usually stores sampled images on the built-in memory chip [4]
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