Object Detection Algorithm Based on Improved Feature Extraction Network

Fig. 3. Node representations of cell structures of ResNet, DensNet, and two-path networks. (a) ResNet network; (b) DensNet network; (c) two-path network

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Fig. 4. Examples of traditional NMS problems. (a) Horses; (b) birds

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Fig. 5. Trend of parameter quantity of feature extraction network with Top-1 error rate and 52, 100, and 133 layers

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Fig. 6. Trend of parameter quantity of feature extraction network with Top-1 error rate and network growth rates of 12, 18, 24, and 48

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Table 1. Structure of feature extraction network

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Table 1. Structure of feature extraction network

Layer	Output size	Detail
Conv1	112×112	7×7,64,stride 2
Conv2	56×56	3×3 max pool,stride 2 $\begin{matrix} [\begin{matrix} 1 \times 1 conv \\ 3 \times 3 conv \\ 1 \times 1 conv \end{matrix}] \end{matrix}$ ×α₁
Conv3	28×28	$\begin{matrix} [\begin{matrix} 1 \times 1 conv \\ 3 \times 3 conv \\ 1 \times 1 conv \end{matrix}] \end{matrix}$ ×α₂
Conv4	14×14	$\begin{matrix} [\begin{matrix} 1 \times 1 conv \\ 3 \times 3 conv \\ 1 \times 1 conv \end{matrix}] \end{matrix}$ ×α₃
Conv5	7×7	$\begin{matrix} [\begin{matrix} 1 \times 1 conv \\ 3 \times 3 conv \\ 1 \times 1 conv \end{matrix}] \end{matrix}$ ×α₄

Table 2. Comparison of complexity of different feature extraction networks
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Table 2. Comparison of complexity of different feature extraction networks
Feature extraction network Depth Parameter /10⁶
VGG-16 16 168
DensNet(K=48) 161 111
ResNet 101 150
Ours(α₁α₂α₃α₄=6,8,16,3; K=48) 100 134

Table 3. Influences of IoU threshold, β parameter, and weighted average on AP (0.5, 0.6, and 0.7 represent different IoU thresholds; w represents weighted average)

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Table 3. Influences of IoU threshold, β parameter, and weighted average on AP (0.5, 0.6, and 0.7 represent different IoU thresholds; w represents weighted average)

Different parameter	A $\begin{matrix} {P^{0 .}}^{5} \end{matrix}$	A $\begin{matrix} {P^{0 .}}^{5 w} \end{matrix}$	A $\begin{matrix} {P^{0 .}}^{6} \end{matrix}$	A $\begin{matrix} {P^{0 .}}^{6 w} \end{matrix}$	A $\begin{matrix} {P^{0 .}}^{7} \end{matrix}$	A $\begin{matrix} {P^{0 .}}^{7 w} \end{matrix}$
Normal NMS	44.37	44.83	39.18	39.67	29.83	30.34
β=2.5, σ=0.4	46.42	46.92	42.83	43.40	34.68	35.24
β=1.67, σ=0.6	46.58	47.11	43.30	43.79	35.21	35.76
β=1.25, σ=0.8	45.93	46.45	41.68	42.21	33.01	33.53

Table 4. Influences of data augmentation and improved NMS mechanism on accuracy
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Table 4. Influences of data augmentation and improved NMS mechanism on accuracy
Detection framwork Backbone Training set Testing set mAP /%
OursNo augmentation No improved NMS ProposedProposedProposed VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012 VOC2007VOC2007VOC2007 79.176.678.0

Table 5. Influences of different epochs on accuracy
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Table 5. Influences of different epochs on accuracy
Nums of epoch Learning rate setting mAP /%
0 No warming up 78.20
2 0.01, 0.1 78.25
345 0.001, 0.01, 0.10.0001, 0.001, 0.01, 0.10.00001, 0.0001, 0.001, 0.01, 0.1 78.3678.6778.71

Table 6. Testing results of different algorithms under VOC2007+VOC2012 training sets

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Table 6. Testing results of different algorithms under VOC2007+VOC2012 training sets

Method		Backbone	Training set	Testing set	mAP/%	Frame rate /(frame·s^-1)
Twostage	Fast R-CNNFaster R-CNNFaster R-CNNMR-CNNIONOurs	VGG-16VGG-16ResNet-101ResNet-101VGG-16Proposed	VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012	VOC2007VOC2007VOC2007VOC2007VOC2007VOC2007	70.073.276.478.276.579.1	0.5072.400.031.252.10
Onestage	YOLOYOLOv2SSD321SSD300*DSOD300DSSD513	GoogleNetDarknet-19ResNet-101VGG-16DS/64-192-48-1ResNet-101	VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012VOC2007+VOC2012	VOC2007VOC2007VOC2007VOC2007VOC2007VOC2007	63.478.677.177.277.781.5	454011.204617.405.50

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Ting Qiao, Hansong Su, Gaohua Liu, Meng Wang. Object Detection Algorithm Based on Improved Feature Extraction Network[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231008

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