摘要
针对水下机器人搭载平台进行深海遗迹探寻所面临的数据样本缺乏、采集图像模糊、嵌入式系统计算能力有限的问题,分别在数据增强、视频增强和目标检测算法压缩等方面提出了有效的解决方案。首先,利用数据增强提高小样本数据的泛化能力,通过迁移学习进行知识迁移,加速模型收敛;然后,基于结构相似性对采集视频进行关键帧图像选择,采用限制对比度直方图拉伸对选择后的关键帧进行实时图像增强;最后,基于多粒度剪枝策略对YOLOV4进行通道和卷积层的双向压缩。实验结果表明,压缩后的YOLOV4模型运算复杂度(BFLOPS)降为10.588,在Jetson TX2嵌入式图像处理器上,对大小为640 pixel×480 pixel的输入图像的平均检测速度可以达到18.2 frame/s。
An effective solution for data enhancement,video enhancement,and target detection algorithm compression is proposed to address the data deficiency,blurred images,and limited computing power of an embedded system of the unmanned underwater vehicle for searching underwater relics.First,the data augmentation was used to improve the generalization ability of small sample,and knowledge transfer was then implemented through transfer learning to accelerate the convergence of models.Then,key frame images were selected from the captured video based on the structural similarity,and the selected key frames were enhanced using the contrast limited adaptive histogram equalization stretching in real time.Finally,based on the multigranularity pruning strategy,YOLOV4 performed bidirectional compression of the channel and convolutional layer.The experimental results show that the operation complexity(BFLOPS)of the compressed YOLOV4 model is reduced to 10.588.The average detection speed for images with a size of 640 pixel×480 pixel on the Jetson TX2 embedded image processor reaches 18.2 frame/s.
作者
张有波
郭威
周悦
徐高飞
李广伟
孙洪鸣
Zhang Youbo;Guo Wei;Zhou Yue;Xu Gaofei;Li Guangwei;Sun Hongming(College of Engineering Science and Technology,Shanghai Ocean University,Shanghai 201306,China;Institute of Deep-Sea Science and Engineering,Chinese Academy of Sciences,Sanya,Hainan 572000,China;University of Chinese Academy of Sciences,Beijing 100049,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第14期278-287,共10页
Laser & Optoelectronics Progress
基金
国家重点研发计划(2020YFC1521704)
海南省自然科学基金(2019RC260)
三亚市院地科技合作项目(2019YD01)。
关键词
图像处理
视频图像增强
模型压缩
嵌入式图像处理器
目标检测
水下机器人
image processing
video enhancement
model compression
embedded image processor
target detection
unmanned underwater vehicle
作者简介
通信作者:郭威,guow@idsse.ac.cn。
