An automatic fishing net detection and recognition method for underwater obstacle avoidance is proposed. In the method, optical gated viewing technology is utilized to obtain high-resolution fishing net images and extend detection distance by suppressing water backscattering and background noise. The fishing net recognition is based on the proposed histograms of slope lines (HSLs) descriptors plus a support vector machine classifier. The extraction of HSL descriptors includes five steps of contrast-limited adaptive histogram equalization, the Gaussian low-pass filtering, the Canny detection, the Hough transform, and weighted vote. In the proof experiments, the detection distance of the fishing net reaches 5.7 attenuation length and the recognition accuracy reaches 93.79%.
Laser range-gated imaging has great potentials in remote night surveillance with far detection distance and high resolution, even if under bad weather conditions such as fog, snow and rain. However, the field of view (FOV) is smaller than large objects like buildings, towers and mountains, thus only parts of targets are observed in one single frame, so that it is difficult for targets identification. Apparently, large FOV is beneficial to solve the problem, but the detection range is not available due to low illumination density in a large field of illumination matching with the FOV. Therefore, a large field-of-view range-gated laser imaging is proposed based on image fusion in this paper. Especially an image fusion algorithm has been developed for low contrast images. First of all, an infrared laser range-gated system is established to acquire gate images with small FOV for three different scenarios at night. Then the proposed image fusion algorithm is used for generating panoramas for the three groups of images respectively. Compared with raw images directly obtained by the imaging system, the fused images have a larger FOV with more detail target information. The experimental results demonstrate that the proposed image fusion algorithm is effective to expand the FOV of range-gated imaging.
Underwater 3D range-gated imaging can extend the detection range over underwater stereo cameras, and also has great potentials in real-time high-resolution imaging than 3D laser scanning. In this paper, a triangular-range-intensity profile spatial correlation method is used for underwater 3D range-gated imaging. Different from the traditional trapezoidal method, in our method gate images have triangular range-intensity profiles. Furthermore, inter-frame correlation is used for video-rate 3D imaging. In addition, multi-pulse time delay integration is introduced to shape range-intensify profiles and realize flexible 3D SRGI. Finally, in experiments, 3D images of fish net, seaweed and balls are obtained with mm-scaled spatial and range resolution.
Underwater range-gated laser imaging (URGLI) still has some problems like un-uniform light, low brightness and contrast. To solve the problems, a variant of adaptive histogram equalization called contrast limited adaptive histogram equalization (CLAHE) is proposed in this paper. In experiment, using the CLAHE and HE to enhance the images, and evaluate the quality of enhanced images by peak signal to noise ratio (PSNR) and contrast. The result shows that the HE gets the images over-enhanced, while the CLAHE has a good enhancement with compressing the over-enhancement and the influence of un-uniform light. The experimental results demonstrate that the CLAHE has a good result of image enhancement for target detection by underwater range-gated laser imaging system.
Three-dimensional super-resolution range-gated imaging (3D SRGI) is a new technique for high-resolution 3D sensing. Up to now, 3D SRGI has been developed with two range-intensity correlation algorithms, including trapezoidal algorithm and triangular algorithm. To obtain high depth-to-resolution ratio of 3D imaging, coding method was developed for 3D SRGI based on the trapezoidal algorithm in 2011. In this paper, we propose the range-intensity coding based on the triangular algorithm and the hybrid range-intensity coding based on the triangular and trapezoidal algorithms. The theoretical models to predict the maximum coding bin number are developed for different coding methods. In the models, the maximum coding bin number is 7 for three coding gate images under the triangular algorithm, and the maximum is extended to 16 under the hybrid algorithm. The coding examples of 7 bins and 16 bins mentioned above are also given in this paper. The comparison among the three coding methods is performed by the depth-to-resolution ratio defined as the ratio between the 3D imaging depth and the product of the range resolution and raw gate image number, and the hybrid coding method has the highest depth-to-resolution ratio. Higher depth-to-resolution ratio means better 3D imaging capability of 3D SRGI.
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