一種平衡準確性以及高效性的顯著性目標檢測深度卷積網(wǎng)絡模型
doi: 10.11999/JEIT190229
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燕山大學電氣工程學院 秦皇島 066004
基金項目: 河北省自然科學基金(F2015203212, F2019203195)
A Deep Convolutional Network for Saliency Object Detection with Balanced Accuracy and High Efficiency
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School of Electrical Engineering, Yan Shan University, Qinhuangdao 066004, China
Funds: The Nature Science Foundation of Hebei Province (F2015203212, F2019203195)
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摘要:
當前的顯著性目標檢測算法在準確性和高效性兩方面不能實現(xiàn)良好的平衡，針對這一問題，該文提出了一種新的平衡準確性以及高效性的顯著性目標檢測深度卷積網(wǎng)絡模型。首先，通過將傳統(tǒng)的卷積替換為可分解卷積，大幅減少計算量，提高檢測效率。其次，為了更好地利用不同尺度的特征，采用了稀疏跨層連接結構及多尺度融合結構來提高模型檢測精度。廣泛的評價表明，與現(xiàn)有方法相比，所提的算法在效率和精度上都取得了領先的性能。
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 整體網(wǎng)絡結構詳表
結構 名稱 類型 輸出尺寸 輸出編號 結構 名稱 類型 輸出尺寸 輸出編號 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 下采樣 cross-layer conv3 rate=24 448$ \times $448$ \times $256 $1'' $ reconv$ \times $3 56$ \times $56$ \times $128 6 convblock2 maxpool 下采樣 concat3 融合 56$ \times $56$ \times $256 $(5'+6) $ reconv$ \times $2 224$ \times $224$ \times $32 2 conv1 降維 56$ \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 降維 224$ \times $224$ \times $32 3 convblock5 maxpool 下采樣 cross-layer conv3 rate=8 224$ \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 下采樣 conv1 降維 28$ \times $28$ \times $256 9 reconv$ \times $3 112$ \times $112$ \times $64 4 upblock1 deconv 上采樣 concat2 融合 112$ \times $112$ \times $128 $(3'+4) $ reconv$ \times $3 112$ \times $112$ \times $64 conv1 降維 112$ \times $112$ \times $64 5 upblock2 deconv 上采樣 448$ \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 不同算法處理時間對比(s)
模型 本文方法 DCL ELD NLDF MST DSR 時間 0.023 1.200 0.300 0.080 0.025 13.580 環(huán)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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