基于深度学习的球形两栖机器人对水下活虾的实时识别研究

Shaolong Wang, Jian Guo, Shuxiang Guo, Qiang Fu, Jigang Xu
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引用次数: 0

摘要

本文将球形机器人用于水产养殖中龙虾的检测与识别。龙虾养殖户经常面临观察、饲养和捕捞等任务,这些任务都是人工完成的,效率低,运营成本高。因此,本文提出了一种基于生成对抗网络和卷积神经网络的水下龙虾实时检测方法,并由球形水陆两栖机器人实现。首先,建立水下龙虾图像数据集,采用改进的GAN算法和数据增量法对数据进行增强预处理;其次,采用轻量级网络MobileNetV2作为SSD网络的骨干,对单镜头多帧检测器(SSD)进行如下改进;在网络预测层,用深度可分离卷积代替标准卷积加速推理;这些参数构建了一个轻量级模型。最后,在水下龙虾数据集上对模型进行训练,并将其部署在一个球形两栖机器人上,绘制出图像增强和算法改进前后训练过程中损失函数值的变化情况。两组实验测试结果表明,该模型优化了水下龙虾的目标识别精度,识别精度达到90.32%。减小的模型尺寸便于模型部署,并且只有24MB大小。该模型在复杂情况下对龙虾的识别具有良好的稳定性和较高的识别精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Study on Real-time Recognition of Underwater Live Shrimp by the Spherical Amphibious Robot Based on Deep Learning
In this paper, spherical robots are used for the detection and identification of lobsters in aquaculture. Lobster farmers are often faced with tasks such as observation, feeding, and fishing, which are all done manually, with low efficiency and high operating costs. Therefore, this paper proposes a real-time underwater lobster detector based on Generative Adversarial Networks and Convolutional Neural Networks, implemented by a spherical amphibious robot. Firstly, the underwater lobster image dataset is established, and the improved GAN algorithm and data increment method are used for data enhancement preprocessing. Secondly, the single-shot multi-frame detector (SSD) is improved as follows, using the lightweight network MobileNetV2 as the backbone of the SSD network; in the network prediction layer, using depthwise separable convolution instead of standard convolution to accelerate inference; compressing the fully connected layer The parameters construct a lightweight model. Finally, the model is trained on the underwater lobster dataset and deployed on a spherical amphibious robot, and the changes in the loss function value during training before and after image enhancement and algorithm improvement are plotted. Two sets of experimental test results show that the model optimizes the target recognition accuracy of underwater lobsters, and the recognition accuracy reaches 90.32%. The reduced model size facilitates model deployment and is only 24MB in size. The model has good stability and high recognition accuracy in identifying lobsters in complex situations.
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