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Binocular Vision of Fish Swarm Detection in Real-time Based on Deep Learning

机译:基于深度学习的鱼群实时双目视觉

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In the field of ocean development, fish swarm detection has significance for AUV's autonomous navigation and fishing industry. Aim to present fish swarm detections are common based on 2D which lack of spatial information, has low accuracy and bad real-time performance, so we proposed systematic fish swarm detection and position method. We used deep learning target detection system to detect fish and used binocular vision position system to position, then fused every fish's 3D information in camera vision to displayed fish swarm spatial information through radar map. Finally, the contrast experiment and is carried out to verify the effectiveness of the proposed method.
机译:在海洋开发领域,鱼群检测对AUV的自主航行和捕鱼业具有重要意义。针对目前基于二维的鱼群检测方法普遍存在的不足,即缺乏空间信息,准确性低,实时性差等问题,提出了系统的鱼群检测与定位方法。我们使用深度学习目标检测系统检测鱼类,并使用双目视觉定位系统进行定位,然后将每条鱼的3D信息融合到相机视觉中,以通过雷达地图显示鱼群的空间信息。最后,进行对比实验并验证了所提方法的有效性。

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