Vehicle Detection in Challenging Scenes Using CenterNet Based Approach  

作　　者：Ayesha Muhammad Javed Iqbal Iftikhar Ahmad Madini OAlassafi Ahmed SAlfakeeh Ahmed Alhomoud 

机构地区：[1]Department of Computer Science,University of Engineering and Technology Taxila,47050,Pakistan [2]Faculty of Computing and Information Technology,King Abdulaziz University Jeddah,21589,Saudi Arabia [3]Faculty of Computing and Information Technology,Northern Border University,Rafha,Saudi Arabia

出　　处：《Computers, Materials & Continua》2023年第2期3647-3661,共15页计算机、材料和连续体（英文）

摘　　要：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.

关 键 词：CenteNet SOTA object detection deep learning MSSD SMOKE DNN KITTI 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]