Aiming at the practical application requirements of small dark target recognition for underwater unmanned aerial vehicles, a underwater laser gating imaging target recognition network based on convolutional neural network is designed to classify and identify underwater multiple targets. The integrated tool HLS transplants the network into the FPGA for circuit implementation. Firstly, the algorithm is designed to verify the realization of the convolutional neural network. Then the underwater target recognition experiment is carried out on the implemented convolutional neural network circuit. The network identification accuracy rate is 94% for the three types of underwater target used in the experiment, which verifies the feasibility of convolutional neural network implementation in FPGA.
KEYWORDS: 3D image processing, 3D acquisition, Target detection, Near infrared, Night vision, Stereoscopy, Pulsed laser operation, Gated imaging, Imaging systems, Night vision systems
Traditional NIR laser night vision systems can only obtain 2D images without target range information, and are also easily affected by fog, rain, snow and foreground/background. To solve the problems above, 3D laser night vision based on range-gated imaging has been developed. This paper reviews 3D range-gated imaging advances and focuses on 3D rangeintensity correlation imaging (GRICI) due to its better real time performance and higher spatial resolution. In GRICI systems, the typical illuminator is eye-invisible pulsed semiconductor laser, and the image sensor chooses gated ICCD or ICMOS with mega pixels and ns-scaled gate time. To realize 3D night vision, two overlapped gate images with trapezoidal or triangular range-intensity profiles are grasped by synchronizing the puled laser and the gated sensor. The collapsed range is reconstructed by the range-intensity correlation algorithm, and furthermore 2D and 3D images can both be obtained at the same frame rate. We have established 3D NIR night vision systems based on triangular GRICI, and the experimental results demonstrate that 3D images realize target extraction from background and through windows or smoke. The range resolution minimum is about less than 0.2m at the range of 1km in our GRICI-NV3000, and the range maximum of 3D imaging is about 5km in our GRICI-NV6000.
Camera traps are commonly used in wildlife monitoring. Traditionally camera traps only capture 2D images of wildlife moving in front of them. However, size information of wildlife is lost, which is vital to determine their ages and genders. To solve this problem, this paper develops a binocular camera trap based on stereo imaging for wildlife detection. The camera trap consists of two cameras, motion sensors, a photosensitive sensor and infrared illumination with the central wavelength of 940nm. Motion sensors output triggers to cameras when animals move past, and then pictures are captured from two different perspectives simultaneously. Meanwhile the photosensitive sensor perceives ambient illumination to control infrared illumination. In this way, the camera trap provides both 2D images of wildlife and their size information obtained by binocular vision. In addition, different from normal binocular cameras placed horizontally, these two cameras are set vertically for the convenience of installation and the expansion of dynamic measure range. As verification, we develop a prototype binocular camera trap to measure a human’s height that is 178cm, and the estimation error approaches 2cm at the distance of 5m.
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