A Deep Convolutional Network for Saliency Object Detection with Balanced Accuracy and High Efficiency
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摘要:
当前的显著性目标检测算法在准确性和高效性两方面不能实现良好的平衡，针对这一问题，该文提出了一种新的平衡准确性以及高效性的显著性目标检测深度卷积网络模型。首先，通过将传统的卷积替换为可分解卷积，大幅减少计算量，提高检测效率。其次，为了更好地利用不同尺度的特征，采用了稀疏跨层连接结构及多尺度融合结构来提高模型检测精度。广泛的评价表明，与现有方法相比，所提的算法在效率和精度上都取得了领先的性能。
Abstract:It is difficult for current salient object detection algorithms to reach a good balance performance between accuracy and efficiency. To solve this problem, a deep convolutional network for saliency object detection with balanced accuracy and high efficiency is produced. First, through replacing the traditional convolution with the decomposed convolution, the computational complexity is greatly reduced and the detection efficiency of the model is improved. Second, in order to make better use of the characteristics of different scales, sparse cross-layer connection structure and multi-scale fusion structure are adopted to improve the detection precision. A wide range of evaluations show that compared with the existing methods, the proposed algorithm achieves the leading performance in efficiency and accuracy.
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表 3 整体网络结构详表
结构 名称 类型 输出尺寸 输出编号 结构 名称 类型 输出尺寸 输出编号 convblock1 reconv$ \times $2 448$ \times $448$ \times $16 1 cross-layer conv3 rate=12 224$ \times $224$ \times $256 $5" $ cross-layer conv3 rate=16 448$ \times $448$ \times $32 $1' $ convblock4 maxpool 下采样 cross-layer conv3 rate=24 448$ \times $448$ \times $256 $1'' $ reconv$ \times $3 56$ \times $56$ \times $128 6 convblock2 maxpool 下采样 concat3 融合 56$ \times $56$ \times $256 $(5'+6) $ reconv$ \times $2 224$ \times $224$ \times $32 2 conv1 降维 56$ \times $56$ \times $128 7 concat1 融合 224$ \times $224$ \times $64 $(1'+2) $ cross-layer conv3 rate=6 56$ \times $56$ \times $256 $7'' $ conv1 降维 224$ \times $224$ \times $32 3 convblock5 maxpool 下采样 cross-layer conv3 rate=8 224$ \times $224$ \times $64 $3′ $ reconv$ \times $3 28$ \times $28$ \times $256 8 cross-layer conv3 rate=18 224$ \times $224$ \times $256 $3" $ concat4 融合 28$ \times $28$ \times $1280 $(1''+3''+5''+7''+8) $ convblock3 maxpool 下采样 conv1 降维 28$ \times $28$ \times $256 9 reconv$ \times $3 112$ \times $112$ \times $64 4 upblock1 deconv 上采样 concat2 融合 112$ \times $112$ \times $128 $(3'+4) $ reconv$ \times $3 112$ \times $112$ \times $64 conv1 降维 112$ \times $112$ \times $64 5 upblock2 deconv 上采样 448$ \times $448$ \times $2 final ross-layer conv3 rate=4 224$ \times $224$ \times $128 $5' $ 
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表 4 F-measure(F-m)和MAE得分表
算法 MSRA ECSSD PASCAL-S SOD HKU-IS F-m MAE F-m MAE F-m MAE F-m MAE F-m MAE 本文方法 0.914 0.045 0.893 0.060 0.814 0.113 0.832 0.119 0.893 0.036 DCL 0.905 0.052 0.890 0.088 0.805 0.125 0.820 0.139 0.885 0.072 ELD 0.904 0.062 0.867 0.080 0.771 0.121 0.760 0.154 0.839 0.074 NLDF 0.911 0.048 0.905 0.063 0.831 0.099 0.810 0.143 0.902 0.048 MST 0.839 0.128 0.653 0.171 0.584 0.236 – – – – DSR 0.812 0.119 0.737 0.173 0.646 0.204 0.655 0.234 0.735 0.140
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表 5 不同算法处理时间对比(s)
模型 本文方法 DCL ELD NLDF MST DSR 时间 0.023 1.200 0.300 0.080 0.025 13.580 环境 GTX1080 GTX1080 GTX1080 Titan X i7 CPU i7 CPU 尺寸 448$ \times $448 300$ \times $400 400$ \times $300 300$ \times $400 300$ \times $400 400$ \times $300
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