Lightweight Object Detection Network Based on Convolutional Neural Network

Yequn Cheng; Yan Wang; Yuying Fan; Baoqing Li

doi:10.3788/LOP202158.1610023

Laser & Optoelectronics Progress, Volume. 58, Issue 16, 1610023(2021)

Lightweight Object Detection Network Based on Convolutional Neural Network

Yequn Cheng^1,2, Yan Wang^1,2, Yuying Fan^1,2, and Baoqing Li^1、*

¹Key Laboratory of Microsystem Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China

²University of Chinese Academy of Sciences, Beijing 100049, China

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Figures & Tables(11)

Fig. 1. Depthwise separable convolution

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Fig. 2. Channel feature interweaving module

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Fig. 3. Adaptive multi-scale weighted feature fusion module

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Fig. 4. Spatial pyramid pooling

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Fig. 5. Overall network structure of BENet

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Fig. 6. Loss curve of BENet and YOLOv3

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Fig. 7. Comparison of the detection results of different algorithms on the VOC dataset. (a) YOLOv3; (b) BENet; (c) YOLOv3 tiny

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Table 1. Related parameters of I-MobileNetv2

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Table 1. Related parameters of I-MobileNetv2

Input size	Operator	Stride	N	Output size
416×416×3	Conv2d	2	1	208×208×32
208×208×32	Bottleneck	1	1	208×208×16
208×208×16	Bottleneck	2	2	104×104×24
104×104×24	Feature interweaving	1	2	104×104×24
104×104×24	Bottleneck	2	3	52×52×32
52×52×32	Feature interweaving	1	2	52×52×32
52×52×32	Bottleneck	2	4	26×26×64
26×26×64	Bottleneck	1	3	26×26×96
26×26×96	Feature interweaving	1	2	26×26×96
26×26×96	Bottleneck	2	3	13×13×160
13×13×160	Bottleneck	1	1	13×13×320
13×13×320	Feature interweaving	1	2	13×13×320
13×13×320	Conv2d	1	1	13×13×1280

Table 2. Comparison of the results of different object detection algorithms on PASCAL VOC dataset

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Table 2. Comparison of the results of different object detection algorithms on PASCAL VOC dataset

Algorithm	Input size	Backbone	mAP/%	FPS	Params/10⁶
Faster-RCNN	600×1000	VGG	73.2	7	138.5
SSD	512×512	VGG	76.8	22	33.1
DSSD	513×513	ResNet-101	81.5	5.5
SSDLite	300×300	MobileNet	72.7	56	6.8
R-FCN	600×1000	ResNet-101	80.5	9
RFBNet	512×512	VGG	82.2	38	34.5
YOLO	448×448		66.4	20	86.7
YOLOv3 tiny	544×544		61.1	116	8.5
YOLOv3	544×544	Darknet 53	80.4	26.5	62.3
BENet	544×544	I-MobileNetv2	78.4	49	7.9

Table 3. Comparison of model size and number of calculations between BENet and YOLOv3
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Table 3. Comparison of model size and number of calculations between BENet and YOLOv3
Algorithm Model size /MB BFLOPs
YOLOv3 tiny (416) 34 5.56
YOLOv3(416) 236 65.86
BENet(416) 32 6.31

Table 4. Ablation experiment on PASCAL VOC dataset
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Table 4. Ablation experiment on PASCAL VOC dataset
BENet baseline With MobileNetv2 With I-MobileNetv2 With SPP With weighted feature fusion mAP /%
√ √ 75.1
√ √ 76.3
√ √ √ 77.6
√ √ √ √ 78.4

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Yequn Cheng, Yan Wang, Yuying Fan, Baoqing Li. Lightweight Object Detection Network Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610023

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