TY -的A2 Minutolo Aniello盟——崔Suxia盟,周于非盟- Wang Yonghui盟,翟Lujun PY - 2020 DA - 2020/01/23 TI -鱼检测使用深度学习SP - 3738108六世- 2020 AB -最近,人类的好奇心已经扩展从土地到天空和大海。除了送人们去探索海洋和外太空，机器人还被设计用于一些对生物危险的任务。以海洋探险为例。目前已有许多自主水下机器人(AUV)的设计项目或竞赛引起了广泛的兴趣。本文作者从以前的一个AUV设计项目中了解到平台升级的必要性，并想分享一个在鱼类检测领域的任务扩展的经验。因为大多数嵌入式系统已经通过快速增长的计算和传感技术得到了改进，这使得它们有可能包含越来越复杂的算法。在水下机器人中，传感器获取周围信息后，如何感知和分析相应的信息以更好地进行判断是水下机器人面临的挑战之一。这个过程可以模拟人类的学习过程。一个具有更多计算能力的先进系统可以促进深度学习特征，利用许多神经网络算法来模拟人脑。提出了一种基于卷积神经网络(CNN)的鱼类检测方法。 The training data set was collected from the Gulf of Mexico by a digital camera. To fit into this unique need, three optimization approaches were applied to the CNN: data augmentation, network simplification, and training process speed up. Data augmentation transformation provided more learning samples; the network was simplified to accommodate the artificial neural network; the training process speed up is introduced to make the training process more time efficient. Experimental results showed that the proposed model is promising, and has the potential to be extended to other underwear objects. SN - 1687-9724 UR - https://doi.org/10.1155/2020/3738108 DO - 10.1155/2020/3738108 JF - Applied Computational Intelligence and Soft Computing PB - Hindawi KW - ER -