摘要
智能车辆道路环境感知中道路目标的准确快速检测,是智能车辆安全辅助驾驶和自动驾驶中的难点。为此,在整体视觉结构模型的基础上,模拟人体由局部到整体的认知机理,并基于原目标局部模型引入结构化信息及概率统计模型,得到更具姿态和尺度适应性的整体视觉结构模型。该模型在弱标注训练样本的基础上可实现目标局部特征区域的自动标注功能,从而获得更具特征描述性的目标特征。实验结果表明,该模型可实现道路典型目标的高检测率和低误码率,且算法效率与经典算法相比有所提高。
Fast and correct detection of on-road objects is a difficult problem m the field of assistant anvmg ano automatic driving of intelligent vehicles. So, based on overall visual structure model, the visual cognitive mechanism from the local to the whole is simulated. The structural model is introduced in the object part model, as well as the statistical model,the overall visual structural model with scale invariance and posture invariance is gained. Through weak-labeled sample image, the model can automatically label the representative local areas so that this model can be more discriminable in detection. The new model is adapted to detect obstacle object in road area. Experimental results show that the object detection algorithm based on the proposed model has higher efficiency than traditional algorithms. It has high detection rate and low error rate.
出处
《计算机工程》
CAS
CSCD
北大核心
2016年第10期26-31,共6页
Computer Engineering
基金
国家自然科学基金青年基金资助项目(61503349)
湖北省自然科学基金资助面上项目(2012FEB6407)
2016年中国地质大学校C类学术创新基地开放基金资助项目(AU2015CJ017)
关键词
目标检测
环境感知
目标模型
智能车辆
结构模型
object detection
environment perception
object model
intelligent vehicle
structure model
作者简介
刘玮（1976-），女，讲师、博士，主研方向为目标识别与检测、数字图像处理；E-mail：liuwei@cug．edu．cn
王新梅，讲师、博士。
魏龙生，讲师、博士。
