基于YOLOv3的水下小目标自主识别

Small Underwater Target Recognition Based on YOLOv3

针对智能水下机器人作业时小目标自主识别的需求,提出基于深度学习的YOLOv3算法,通过对水下机器人实采数据进行神经网络权重训练,实现对水下小目标物快速、精确的识别与分类,从而解决在复杂的水下地形和未知作业环境中对水下目标识别问题。并分析算法学习率在水下海珍品数据集上对损失函数值的影响。实验结果表明,基于YOLOv3算法的水下海珍品的目标检测具有强实时性与高准确率,所有目标类别查准率高达99%,物体的查全率在90%以上,可达35帧/秒的检测速率;在网络训练过程中调整学习率有利于加速并降低损失函数值。

For the demand of autonomous recognition for small objects in subsea operations by remote operated vehicles(ROVs),a YOLOv3 algorithm based on deep learning is proposed.ROVs are trained according to neural network weight of the data collected in ROV fetching competition,in order to identify and classify small underwater objects fast and precisely,which will solve the underwater-object-recognition problem in complex and unknown subsea operation environment.The effect of algorithm learning rate on loss functions in the seafood data set is analyzed.The results show that underwater object recognition and classification of seafood based on YOLOv3 algorithm is of high real time as well as precision.The precision ratio of all objects classified reaches up to ninety-nine percent,and the recall ratio is above ninety percent.The estimating speed can reach up to 35 frames per second(FPS).It also shows that adjusting learning rate during network training process can help decrease loss functions.