基于Slim-YOLOv4与嵌入式设备的无人机检测

doi:10.16180/j.cnki.issn1007-7820.2023.05.009

Abstract

Abstract:

In order to realize real-time detection of UAV on resource-constrained embedded devices, this study proposes a lightweight detection network, Slim-YOLOv4, based on YOLOv4. In this network, Ghostnet is used to replace the main feature extraction part of the original YOLOv4, the 3×3 convolution in the feature fusion part is replaced by the deep detachability convolution and the activation function in the deep detachability convolution is optimized to reduce the number of parameters and computation of the network and accelerate the convergence of the network. The experimental results show that the accuracy of the Slim-YOLOv4 is 91.6%, which is 1.6% lower than that of the original YOLOv4, but the weight file of original YOLOv4 is up to 250 MB. On the premise of not affecting the robustness, the weight file size of Slim-YOLOv4 is only 42 MB, which is better than the 108 MB of Fan-RCNN model and 53 MB of Mobilenetv3 model. The number of images processed per second by the proposed method reaches 31.2 frames·s^-1 on PCS and 37.6 frames·s^-1on embedded devices, proving that the proposed method can be deployed on embedded devices for real-time detection of UAVs.

Key words: lightweight network, YOLOv4, inspection drone, embedded platform, deep learning, Ghostnet, ELU, real-time detection

CLC Number:

TP391

ZHENG Yuheng,FU Dongxiang. UAV Detection Based on Slim-YOLOv4 with Embedded Device[J].Electronic Science and Technology, 2023, 36(5): 55-61.

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References 20

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输入	操作	扩张通道	输出通道	SE	步长
224×224×3	Conv2d 3×3	-	16	-	2
112×112×16	G-bneck	16	16	-	1
112×112×16	G-bneck	48	24	-	2
56×56×24	G-bneck	72	24	-	1
56×56×24	G-bneck	72	40	1	2
28×28×40	G-bneck	120	40	1	1
28×28×40	G-bneck	240	80	-	2
14×14×80	G-bneck	200	80	-	1
14×14×80	G-bneck	184	80	-	1
14×14×80	G-bneck	184	80	-	1
14×14×80	G-bneck	480	112	1	1
14×14×112	G-bneck	672	112	1	1
14×14×112	G-bneck	672	160	1	2
7×7×160	G-bneck	960	160	-	1
7×7×160	G-bneck	960	160	1	1
7×7×160	G-bneck	960	160	-	1
7×7×160	G-bneck	960	160	1	1
7×7×160	Conv2d 1×1	-	960	-	1
7×7×960	AvgPool 7×7	-	-	-	-
1×1×960	Conv2d 1×1	-	1 280	-	1
1×1×1 280	FC	-	1 000	-	-

模型	准确率 /%	召回率 /%	Weights /MB	FPS /frames·s^-1
Faster-RCNN	90.7	88.3	108	3.1
YOLOv4	93.2	90.5	250	5.8
Gh-YOLOv4	91.5	90.1	150	15.4
Slim-YOLOv4	91.6	86.9	42	31.2

主干网络	准确率 /%	召回率 /%	F1 /%	Weights /MB	FPS /frames·s^-1
Mobilenetv3	89.8	90.6	0.90	53	25.54
Ghostnet	91.6	86.9	0.89	42	31.20

模型	FPS/frames·s^-1
YOLOv4	10.3
Gh-YOLOv4	20.2
Mobilenetv3-YOLOv4	29.3
Slim-YOLOV4	37.6

类别	Faster R-CNN	YOLOV4	Slim-YOLOv4
Cat	98.2	97.6	96.3
Person	90.3	93.2	90.8
Aero	95.3	98.5	97.8
Car	88.4	92.6	90.0
Bus	97.2	97.9	94.4
Dog	95.6	96.2	95.5
Motorbike	92.1	94.5	90.6
Train	98.5	98.7	96.0
Horse	92.8	96.5	93.6
Table	70.8	86.3	83.0
Sheep	83.9	88.4	86.4
Tv	83.3	94.6	90.9
Boat	86.9	91.2	86.4
Chair	79.6	90.6	86.9
Bird	93.2	97.4	92.3
Bicycle	90.8	93.6	87.8
Bottle	72.4	80.3	78.8
Sofa	88.8	94.8	88.6
Plant	74.7	79.8	76.8
Cow	98.6	98.1	95.3
mAP/%	88.6	93.0	89.9

UAV Detection Based on Slim-YOLOv4 with Embedded Device

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