文章提出了一种基于深度学习的MSSD(Modified Single Shot multibox Detector)目标检测方法。文章阐述了SSD方法的模型与工作原理,SSD方法采用多尺度的特征图预测物体,使用具有较大感受野的高层特征图预测大物体,具有较小感受野的低层...文章提出了一种基于深度学习的MSSD(Modified Single Shot multibox Detector)目标检测方法。文章阐述了SSD方法的模型与工作原理,SSD方法采用多尺度的特征图预测物体,使用具有较大感受野的高层特征图预测大物体,具有较小感受野的低层特征图预测小物体。使用的低层网络的特征图预测小物体时,由于缺乏高层语义特征,所以导致SSD对小物体的检测效果较差。文章提出了一种MSSD模型,把原有的VGG网络替换为深度残差网络,采用了特征金字塔网络模块对高层语意信息和低层细节信息融合,并通过1 000张图像数据集测试,对比MSSD方法与SSD方法在目标检测上的物体检索能力与检测精度。结果表明,MSSD方法比SSD方法准确率高、速度快。展开更多
Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road accidents.The major obstacles in automatic detection of tiny vehicles are due...Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road accidents.The major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of objects.This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area.In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely CenterNet.Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded area.Primarily there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object detection.We in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by one.We train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP respectively.Effectiveness of CenterNet can be justified through the huge improved performance.The performance of our model is evaluated on KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature extractors.We also compare our model with other SOTA techniques.展开更多
文摘Contemporarily numerous analysts labored in the field of Vehicle detection which improves Intelligent Transport System(ITS)and reduces road accidents.The major obstacles in automatic detection of tiny vehicles are due to occlusion,environmental conditions,illumination,view angles and variation in size of objects.This research centers on tiny and partially occluded vehicle detection and identification in challenging scene specifically in crowed area.In this paper we present comprehensive methodology of tiny vehicle detection using Deep Neural Networks(DNN)namely CenterNet.Substantially DNN disregards objects that are small in size 5 pixels and more false positives likely to happen in crowded area.Primarily there are two categories of deep learning models single-step and two-step.A single forward pass model is the one in which detection is performed directly to possible location over dense sampling,wherein two-step models incorporated by Region proposals followed by object detection.We in this research scrutinize one-step State of the art(SOTA)model CenteNet as proposed recently with three different feature extractor ResNet-50,HourGlass-104 and ResNet-101 one by one.We train our model on challenging KITTI dataset which outperforms in comparison with SOTA single-step technique MSSD300∗which depicts performance improvement by 20.2%mAPandSMOKEby with 13.2%mAP respectively.Effectiveness of CenterNet can be justified through the huge improved performance.The performance of our model is evaluated on KITTI(Karlsruhe Institute of Technology and Toyota Technological Institute)benchmark dataset with different backbones such as ResNet-50 gives 62.3%mAP ResNet-10182.5%mAP,last but not the least HourGlass-104 outperforms with 98.2%mAP CenterNet-HourGlass-104 achieved high mAP among above mentioned feature extractors.We also compare our model with other SOTA techniques.
