计算机科学 ›› 2022, Vol. 49 ›› Issue (3): 211-217.doi: 10.11896/jsjkx.201200019
张侣, 周博文, 吴亮红
ZHANG Lyu, ZHOU Bo-wen, WU Liang-hong
摘要： SSD(Single Shot Multibox Detector) 是一种基于卷积神经网络的单阶检测算法,相比双阶检测算法,它在保证一定精度的同时显著提高了检测速度,但仍难以满足很多实际应用,尤其是在小目标检测任务中,检测精度更是难以满足需求。针对该不足,文中提出了一种基于改进残差结构与卷积注意力模块的特征提取网络Res-Am CNN (Residual with Attention Module Convolutional Neural Networks),大幅提高了网络的特征提取能力,并在原始SSD金字塔结构中引入上采样加法融合 (Additive Fusion with Upsample,AFU) 来进行特征融合,增强了浅层特征的表征能力。在 PASCAL VOC数据集上的实验结果表明,相比原始SSD网络和主流的检测网络,Res-Am&AFU SSD (SSD with Res-Am CNN and AFU) 网络在VOC测试集上的平均精度均值(mean Average Precision,mAP) 达到69.1%,在精度上领先单阶网络,接近双阶网络,在检测速度上远快于双阶网络。在小目标测试集上的实验结果表明,Res-Am&AFU SSD网络的mAP为67.2%,比原始SSD提高了9.4%,且该方法具有更加灵活、无需预训练等优点。
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