基于深度特征的多方向目标检测研究

Multi-directional target detection based on depth features

近年来目标检测成为计算机视觉技术的重要分支,广泛应用于医学、军事、城轨等领域,随着卫星和遥感技术的进步,其获取的图像蕴含着丰富的信息,因此对这些图像中目标自动检测和理解变得至关重要。但是遥感影像中目标方向随意、密集等,传统目标检测方法容易导致漏检错检,针对此问题,该文提出多卷积核特征组合自适应区域生成网络(multi-convolution kernel feature combination adaptive region proposal network,MFCARPN)算法进行多方向检测,该算法引入多个不同卷积核提取特征,可以根据目标的差异性自适应地学习每个卷积核特征的权重参数,得到和目标更加匹配的特征图,同时通过结合目标原始特征使分类回归模型参数可以依据目标之间的差异性动态变化,提高区域生成网络(region proposal network,RPN)自适应能力。实验表明其在DOTA标准数据集的平均精度均值(mean average precision,mAP)达到75.52%,相较于GV算法提高0.5个百分点,由此证明了该算法的有效性。

In recent years,target detection,as an important branch of computer vision technology,has been widely applied in fields such as medicine,military affairs,and urban rail transit.As satellite and remote sensing technologies advance,images obtained using these technologies contain abundant information.This makes it crucial to conduct automatic target detection and understanding of these images.However,due to the random directions and dense distribution of targets in remote sensing images,conventional methods are prone to lead to missing or incorrect detection.In response,this study proposes a multi-convolution kernel feature combination-based adaptive region proposal network(MFCARPN)algorithm for multi-directional detection.This algorithm introduces multiple convolution kernel features for target extraction.The weight parameters of these convolution kernel features can be determined through adaptive learning according to the differences between the targets,yielding the characteristic patterns that match better with targets.Meanwhile,in combination with the original features of the targets,the parameters of the classification and regression model vary dynamically according to the difference between targets.Thus,the RPN’s adaptive ability can be improved.The experimental results indicate that the mAP of the standard dataset DOTA reached up to 75.52%,which is 0.5 percentages higher than that of the baseline algorithm GV.Therefore,the MFCARPN algorithm proposed in this study proves effective.