Light-Weight Object Detection Networks for Embedded Platform

Fig. 2. Basic structure of depth separable convolution network. (a) Standard convolution filter; (b) depthwise convolution filter; (c) pointwise convolution filter

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Fig. 3. Architecture of MTYOLO network

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Fig. 4. Comparison of proposed algorithm and Tiny-Yolo algorithm. (a) Input image; (b) Tiny-Yolo algorithm; (c) proposed algorithm

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Table 1. Comparison of detection accuracy after merging different layers
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Table 1. Comparison of detection accuracy after merging different layers
Conv2 Conv3 Conv4 Conv5 Conv6 Conv7 Conv10 P_mAP
× × × × × × × 54.2
× × × √ √ × × 56.7
× × √ √ √ × × 57.6
× √ √ √ √ × × 58.4
√ √ √ √ √ × × 58.2
× √ √ √ √ √ × 58.9
× √ √ √ √ √ √ 59.3

Table 2. Comparison of experimental results of MTYOLO with BN layer
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Table 2. Comparison of experimental results of MTYOLO with BN layer
BN layer P_mAP Speed /(frame·s^-1)
× 52.7 31
√ 56.2 29

Table 3. Comparison of experimental results of different network architectures
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Table 3. Comparison of experimental results of different network architectures
Model Speed /(frame·s^-1) Model size /MB
Yolo-v2 3 193
Tiny-Yolo 18 61
Using feature map fusion 15 63
Using pointwise 23 50
Using depthwise 29 41

Table 4. Comparison of detection results on VOC dataset by proposed algorithm and Tiny-Yolo

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Table 4. Comparison of detection results on VOC dataset by proposed algorithm and Tiny-Yolo

Method	P_mAP	FPS	aero	bike	bird	boat	bottle	bus	car	cat	chair
Tiny-Yolo	54.2	18	57.4	67.5	44.9	34.8	20.4	67.5	62.9	67.4	32.0
MTYOLO	57.25	29	65.3	74.2	49.6	39.1	30.2	69.8	65.8	69.5	34.2
Method	cow	table	dog	horse	mbike	person	plant	sheep	sofa	train	TV
Tiny-Yolo	53.7	58.1	61.6	70.5	69.1	58.0	27.8	52.8	51.1	68.5	57.4
MTYOLO	56.2	59.3	63.5	72.1	70.2	59.2	29.8	55.1	53.6	69.1	59.2

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Jiahua Cui, Yunzhou Zhang, Zheng Wang, Jiwei Liu. Light-Weight Object Detection Networks for Embedded Platform[J]. Acta Optica Sinica, 2019, 39(4): 0415006

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