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B-PesNet: Smoothly Propagating Semantics for Robust and Reliable Multi-Scale Object Detection for Secure Systems

Computer Modeling in Engineering & Sciences 2022年第9期132卷 1039-1054页

作者： Yunbo Rao Hongyu Mu Zeyu Yang Weibin Zheng Faxin Wang Jiansu Pu Shaoning Zeng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhou313000China

Multi-scale object detection is a research hotspot,and it has critical applications in many secure *** the object detection algorithms have constantly been progressing recently,how to perform highly accurate and reliable multi-class object detection is still a challenging task due to the influence of many factors,such as the deformation and occlusion of the object in the actual *** more interference factors,the more complicated the semantic information,so we need a deeper network to extract deep ***,deep neural networks often suffer from network *** prevent the occurrence of degradation on deep neural networks,we put forth a new model using a newly-designed Pre-ReLU,which inserts a ReLU layer before the convolution layer for the sake of preventing network degradation and ensuring the performance of deep *** structure can transfer the semantic information more smoothly from the shallow to the deep ***,the deep networks will encounter not only degradation,but also a decline in ***,to speed up the two-stage detector,we divide the feature map into many groups so as to diminish the number of ***,calculation speed has been enhanced,achieving a balance between speed and *** mathematical demonstration,a balanced loss(BL)is proposed by a balance factor to decrease the weight of the negative sample during the training phase to balance the positives and ***,our detector demonstrates rosy results in a range of experiments and gains an mAP of 73.38 on PASCAL VOC2007,which approaches the requirement of many security systems.

关键词： Object detection Pre-ReLU CNN balanced loss

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