摘要：

针对可变形部件模型在夜间车辆检测中精确度低、检测速度慢的问题,提出基于可变部件改进模型的检测方法。在训练阶段采用Gamma预处理对夜间车辆样本进行校正,得到物体的梯度模型。在测试阶段利用一种基于(R-B)色差特征的显著性区域检测方法,通过减少待检测区域的面积,降低运算复杂度。针对夜间部分场景出现遮挡的情况,采用一种自适应权重的参数分配策略,给重要的特征部件分配较大的权重值。实验结果表明,改进后的检测方法准确率达95.12%,召回率达91.50%,平均每帧检测时间为48 ms,具有较好的实时性和鲁棒性。

关键词: 可变形部件模型, Gamma预处理, WPCA特征降维, 显著性区域检测, 自适应权重

Abstract:

In order to solve the problem of low accuracy and slow detection speed of Deformable Parts Model(DPM) at night,an improved method based on DPM is proposed.In the training stage,use the Gamma preprocessing to correct the vehicle samples at night,and train the gradient model of the objects.In the testing phase,a method of saliency region detection based on (R-B) chromatic aberration difference is proposed,which reduces the computation complexity by decreasing the area of the region to be detected.A parameter allocation of adaptive weight is proposed,which assigns large weight values to the important feature parts.Experimental results show that the improved detection method has a precision rate of 95.12%,a recall rate of 91.50%,an average detection time of 48 ms per frame,and it has better real-time performance and robustness.

Key words: Deformable Parts Model(DPM), Gamma preprocessing, WPCA feature reduction, saliency region detection, adaptive weight

中图分类号: 

• TP391.41