Object Detection Method Based on Improved YOLO Lightweight Network

Chengyue Li; Jianmin Yao; Zhixian Lin; Qun Yan; Baoqing Fan

doi:10.3788/LOP57.141003

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 14 >Page 141003 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 14, 141003 (2020)

Object Detection Method Based on Improved YOLO Lightweight Network

Chengyue Li^1、2, Jianmin Yao^{1、2、3、*}, Zhixian Lin^1、2, Qun Yan^1、2, and Baoqing Fan^1、2

Author Affiliations

¹Flat Panel Display National and Local Joint Engineering Laboratory, National University Science Park Sunshine Technology Building, Fuzhou University, Fuzhou, Fujian 350116, China

²College of Physics and Information Engineering, Fuzhou University, Fuzhou, Fujian 350116, China

³Jinjiang Bosen Electronic Technology Co., Ltd., Quanzhou, Fujian 362200, China

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DOI: 10.3788/LOP57.141003 Cite this Article Set citation alerts

Chengyue Li, Jianmin Yao, Zhixian Lin, Qun Yan, Baoqing Fan. Object Detection Method Based on Improved YOLO Lightweight Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141003 Copy Citation Text

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Fig. 1. YOLOv3 network structure

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Fig. 2. Structural unit of YOLOv3. (a) Convolutional unit; (b) ResNet unit; (c) Convolutional Set unit

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Fig. 3. Improved backbone network

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Fig. 4. Improved dense network module

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Fig. 5. Improved spatial pyramid pooling network module

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Fig. 6. DS-YOLO network structure. (a) Network structure; (b) DC-SPP unit; (c) Conv-Residual unit

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Fig. 7. YOLOv3tiny test results

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Fig. 8. DS-YOLO test results

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Fig. 9. YOLOv3 test results

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Method	Weight /MB	BFLOPS	mAP /%	Speed /(frame/s)
Base-YOLO	17.2	7.8	65.9	150
YOLOv3	236	65.86	78.3	30

Table 1. Comparison of Base-YOLO and YOLOv3

Method	Weight /MB	BFLOPS	mAP /%	Speed /(frame/s)
Base-YOLO	17.2	7.8	65.9	150
D-YOLO	24.6	8.54	67.7	143

Table 2. Comparison of D-YOLO and Base-YOLO

Method	Weight /MB	BFLOPS	mAP /%	Speed /(frame/s)
Base-YOLO	17.2	7.8	65.9	150
S-YOLO	17.5	7.92	67.1	147

Table 3. Comparison of S-YOLO and Base-YOLO

Method	AP																																			mAP
Method	Aero	Bike	Bird	Boat	Bottle	Bus		Car		Cat		Chair		Cow		Table		Dog		Horse		Mbike		Person		Plant		Sheep		Sofa		Train		Tv		mAP
YOLO-v3tiny	65.3	70	43.8	47	24.9		68.9		74.7		65.7		33.4		53.7		49		61		75.3		72.1		69.1		26.9		59		50.9		75		60.8		57.3
DS-YOLO	78.3	78.9	60.6	62.8	51		76.3		85.5		76.7		49.4		70.8		66.1		71.3		80		79.9		80.9		36.9		66.8		66.2		81.7		68		69.4
YOLO-v3	84.5	85	77.2	68.8	65.4		85.2		86.3		86.3		62.3		79.9		74.8		85.7		87.4		86.2		81.1		50.8		80.2		77.4		82.7		75.6		78.1

Table 4. Comparison of DS-YOLO and YOLO%

Method (size)	Year	Base network	mAP /%	Speed /(frame/s)
Faster RCNN	2015	VGG16	73.2	7
SSD(300)SSD(300)SSDLite(300)DSSD(321)STDN(300)	20162017201720172018	VGG16MobileNetv2MobileNetResNet-101DenseNet-169	74.377.472.778.678.1	4650569.541.5
YOLOv2(416)YOLOv3tiny(416)YOLOv3(416)	201720182018	Darknet19-Darknet53	76.857.178.3	6716830
DS-YOLO	2019	-	69.4	141

Table 5. Comparison of DS-YOLO and others algorithms in VOC2007, 2012

Method	Weight /MB	BFLOPS
YOLOv3tinyYOLOv3	34236	5.5765.86
DS-YOLO	25	8.58

Table 6. Comparison of DS-YOLO and YOLO

Method	AP, IoU			AP, Area
Method	0.5∶0.95	0.5	0.75	S	M	L
YOLOv3tinySSD	14.423.2	33.141.2	16.323.4	3.25.3	18.523.2	30.239.6
DS-YOLOYOLOv3	20.431.1	40.355.3	18.135.2	4.814.2	21.334.1	34.746.4

Table 7. Comparison of DS-YOLO and YOLO、SSD in COCO

Chengyue Li, Jianmin Yao, Zhixian Lin, Qun Yan, Baoqing Fan. Object Detection Method Based on Improved YOLO Lightweight Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141003

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