基于无预训练卷积神经网络的红外车辆目标检测

Infrared Vehicle Target Detection Based on Convolutional Neural Network without Pre-training

为解决基于卷积神经网络的目标检测算法对预训练权重的过度依赖,特别是数据稀缺条件下的红外场景目标检测,提出了融入注意力模块来缓解不进行预训练所带来的检测性能下降的方法。本文基于YOLO v3算法,在网络结构中融入模仿人类注意力机制的SE和CBAM模块,对提取的特征进行通道层面和空间层面的重标定。根据特征的重要程度,自适应地赋予不同权重,最终提升检测精度。在构建的红外车辆目标数据集上,注意力模块能够显著提升无预训练卷积神经网络的检测精度,融入了CBAM模块的网络检测精度为86.3 mAP。实验结果证明了注意力模块能够提升网络的特征提取能力,使网络摆脱对预训练权重的过度依赖。

To tackle the over-dependence of convolutional neural network-based target detection algorithms on pre-training weights,especially for target detection of infrared scenarios under data-sparse conditions,the incorporation of attention modules is proposed to alleviate the degradation of detection performance owing to the absence of pre-training.This paper is based on the YOLO v3 algorithm,which incorporates SE and CBAM modules in a network that mimics human attentional mechanisms to recalibrate the extracted features at the channel and spatial levels.Different weights are adaptively assigned to the features according to their importance,which ultimately improves the detection accuracy.On the constructed infrared vehicle target dataset,the attention module significantly improved the detection accuracy of the non-pre-trained convolutional neural network.Furthermore,the detection accuracy of the network incorporating the CBAM module was 86.3 mAP,demonstrating that the attention module can improve the feature extraction ability of the network and free the network from over-reliance on the pretrained weights.