摘要
高速无人机巡查系统在实际应用中面临图像处理速度慢、准确度低等问题。针对这些挑战,文章设计了一种改进的图像处理算法。该算法结合了深度学习和传统计算机视觉技术,通过优化网络结构、引入注意力机制和多尺度特征融合等方法,显著提高了处理速度和准确率。实验结果表明,改进算法在处理速度上比基准算法提升了40%,目标检测准确率提高了15%,有效解决了高速场景下的图像模糊和目标识别困难等问题,为高速无人机巡查系统的实际应用提供了有力支持。
High-speed UAV inspection system faces the problems of slow image processing speed and low accuracy in practical application.Aiming at these challenges,an improved image processing algorithm is designed in this paper.The algorithm combines deep learning and traditional computer vision technology,and significantly improves the processing speed and accuracy by optimizing the network structure,introducing attention mechanism and multi-scale feature fusion.The experimental results show that the improved algorithm improves the processing speed by 40%compared with the benchmark algorithm,and the target detection accuracy by 15%.It effectively solves the problems such as image blur and target recognition difficulties in high-speed scenes,and provides strong support for the practical application of high-speed UAV inspection system.
作者
王永超
WANG Yongchao(Gansu Hengxin Equipment Leasing Co.,Ltd.,Lanzhou 730000,China)
关键词
高速无人机
图像处理
深度学习
目标检测
算法优化
high-speed UAV
image processing
deep learning
target detection
algorithm optimization
