Underwater Target Detection and 3D Reconstruction System Based on Binocular Vision

Abstract

To better solve the problem of target detection in marine environment and to deal with the difficulty of 3D reconstruction of underwater target, a binocular vision-based underwater target detection and 3D reconstruction system is proposed in this paper. Two optical sensors are used as the vision of the system. Firstly, denoising and color restoration are performed on the image sequence acquired by the vision of the system and the underwater target is segmented and extracted according to the image saliency using the super-pixel segmentation method. Secondly, aiming to reduce mismatch, we improve the semi-global stereo matching method by strictly constraining the matching in the valid target area and then optimizing the basic disparity map within each super-pixel area using the least squares fitting interpolation method. Finally, based on the optimized disparity map, triangulation principle is used to calculate the three-dimensional data of the target and the 3D structure and color information of the target can be given by MeshLab. The experimental results show that for a specific size underwater target, the system can achieve higher measurement accuracy and better 3D reconstruction effect within a suitable distance.

Automatic detection of fish and tracking of movement for ecology

“…We suggest that these methods are transferable to other types of fish corridors and other habitats, such as the mangrove, seagrass and coral reef continuum (Spampinato et al, 2008;Olds et al, 2018;Francisco, Nührenberg & Jordan, 2020). Further development of these models and architectures, such as integrated OD and OT with stereo video (Huo et al, 2018) and pairwise comparisons of detections (Guo et al, 2020), will likely lead to improvements in accuracy. Continual improvements in accuracy will provide a rigorous framework to study and quantify fish connectivity in the wild.…”

The Use of Saliency in Underwater Computer Vision: A Review

“…On the other hand, it suggests that saliency, although being a somewhat subjective notion, is a powerful concept that has still much to say in underwater vision. [21] 2018 Object detection and CNN-based classification Itti Atallah et al [23] 2005 Object detection Entropy-based Wang et al [24] 2013 Detection & Segmentation Itti Chen et al [27] 2014 Object detection Spectral residual Chuang et al [48] 2016 Initialization of object recognition Phase Fourier Transform Zhu et al [34] 2017 Detection & Segmentation Saliency map based on contrast, position, and correspondence Sanchrez-Torres et al [99] 2018 Segmentation Ad hoc based on morphological operators Huo et al [26] 2018 Detection & 3D Reconstruction Aggregation of salient superpixels Kumar et al [36] 2019 Shape reconstruction using edge-based active contours Itti Chen et al [40] 2019 Segmentation using region-based active contours HFT Barat et al [35] 2010 Segmentation using active contours featuring saliency in initialization Itti Kumar et al [31] 2019 Moving object detection Multiple frames difference Zhu et al [50] 2019 Template Matching Spectral residual Jian et al [51] 2018 Object detection QDWB Jian et al [53] 2018 Object detection QDWB + PD + LC Johnson-Roberson et al [47] 2010 Classification Entropy-based Cong et al [32] 2019 Saliency-based Object Detection Saliency map obtained by Deep Convolutional Neural Network Harrison et al [56] 2011 Texture segmentation Co-occurence matrices and ensemble of distance…”

“…Then, the computed value is exploited to define the combination coefficients in Equation 9and, accordingly, the final saliency map. In Reference [26], Huo et al propose a system to perform object detection and 3D reconstruction of targets observed in optical videos. They employ saliency first to identify the salient regions in the image, and to exploit the result to perform foreground object segmentation later.…”

Radiometric Invariant Dense Disparity Estimation for Real Time Stereo Correspondence

“…Two non-parametric transforms are proposed for visual correspondence in (Ramin and Woodfill, 1994). Mutual information have been used for the radiometric invariant visual correspondence in (Mustafa and Kalkan, 2015), (Guanying et al, 2018). A contrast invariant local stereo correspondence employs different spatial frequency channels in (Ogale and Aloimonos, 2005).…”