Detection Algorithm of Recyclable Garbage Based on Improved YOLOv5s

Anneng Luo; Haibin Wan; Zhiwei Si; Tuanfa Qin

doi:10.3788/LOP220603

Journals >Laser & Optoelectronics Progress >Volume 60 >Issue 10 >Page 1010010 > Article

Laser & Optoelectronics Progress
Vol. 60, Issue 10, 1010010 (2023)

Detection Algorithm of Recyclable Garbage Based on Improved YOLOv5s

Anneng Luo¹, Haibin Wan^1,2,*, Zhiwei Si¹, and Tuanfa Qin^1,2

Author Affiliations

¹School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi , China

²Guangxi Key Laboratory of Multimedia Communications and Network Technology, Nanning 530004, Guangxi , China

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DOI: 10.3788/LOP220603 Cite this Article Set citation alerts

Anneng Luo, Haibin Wan, Zhiwei Si, Tuanfa Qin. Detection Algorithm of Recyclable Garbage Based on Improved YOLOv5s[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010010 Copy Citation Text

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Fig. 1. YOLOv5s network structure

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Fig. 2. Basic unit of ShuffleNet v2

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Fig. 3. DW module unit

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Fig. 4. Structure diagram. (a) Schematic of Neck fusion; (b) schematic of improved YOLOv5s overall network

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Fig. 5. Statistic on number of labels in each class on the dataset

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Fig. 6. Original image and image obtained by modules.(a) Original image; (b) image obtained by CBS module; (c) image obtained by S-b module

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Fig. 7. Comparison of indicators between the improved model and YOLOv5s in training process

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Serial number	n	Params	Module	Configuration	Output size
0	1	3520	Focus	［3，32，3］	32×320×320
1	1	3968	S-b	［32，64，2］	64×160×160
2	1	2528	S-a	［64，64，1］	64×160×160
3	1	14080	S-b	［64，128，2］	128×80×80
4	3	27456	S-a	［128，128，1］	128×80×80
5	1	52736	S-b	［128，256，2］	256×40×40
6	3	104064	S-a	［256，256，1］	256×40×40
7	1	203776	S-b	［256，512，2］	512×20×20
8	1	656896	SPP	［512，512，5，9，13］	512×20×20
9	1	134912	S-a	［512，512，1］	512×20×20

Table 1. Configuration of backbone network structure and parameters

Backbone	Neck	P /%	R /%	mAP_0.5 /%	mAP_0.5∶0.95 /%	Parameters /10⁶	Memory /MB
		89.37	85.18	92.09	68.88	7.09	13.7
√		88.85	89.43	93.36	68.10	4.08	8.03
	√	88.41	89.15	93.03	71.40	5.69	11.05
√	√	90.12	89.95	94.01	71.30	2.68	5.34

Table 2. Ablation experiment data of general category

Class	P /%		R /%		AP /%
Class	YOLOv5s	Proposed model	YOLOv5s	Proposed model	YOLOv5s	Proposed model
Edible oil barrels	93	94.1	91.5	97.9	97.2	97.4
Pop cans	84.4	90.1	76.3	85.9	89	91.8
Cartons	90.8	91.4	91.7	93.5	96.3	97.3
Metal cans	81.8	80.9	78.9	85.3	84	88.7
Beverage bottles	77.3	81.7	74.4	76.2	82.9	86.1
Plugs and wires	82.6	86.5	67.6	78.2	80.6	88.2
Book and paper	94.7	95	94.6	97.5	98.1	98.2
Pans	95.9	93.3	87.2	91.7	96.4	97.7
Scissors	97.2	90.4	89.6	93.3	97.1	95.2
Bowls	96	97.8	100	100	99.3	99.5

Table 3. Experimental data for ablation of individual class

Model	P /%	R /%	mAP_0.5 /%	Parameter /10⁶	Memory /MB
Faster-RCNN（ResNet50）	68.50	94.15	92.32	28.3	108.48
YOLOv3	88.47	74.25	85.83	61.9	235.71
YOLOv4	87.83	85.60	88.50	63.9	244.48
YOLOv5s	89.40	85.20	92.10	7.09	13.70
YOLOx-s	92.13	92.53	94.40	8.94	34.29
Proposed model	90.12	89.95	94.01	2.68	5.34

Table 4. Comparison experiments with common models

Image size	Model	Preprocessing time /ms	Inference time /ms	NMS time /ms	FP time /ms
640×640	Yolov5s	1.6	182.3	10.1	194
640×640	Proposed model	1.5	160.2	9.9	171.6
512×512	Yolov5s	1.1	123.3	9.2	133.6
512×512	Proposed model	1.1	108.2	8.8	118.1
416×416	Yolov5s	0.9	82.9	8.0	91.8
416×416	Proposed model	0.9	73.2	7.4	81.5
320×320	Yolov5s	0.8	56.0	6.5	63.3
320×320	Proposed model	0.7	48.5	5.6	54.8

Table 5. Comparison of processing time at Jeson Nano

Anneng Luo, Haibin Wan, Zhiwei Si, Tuanfa Qin. Detection Algorithm of Recyclable Garbage Based on Improved YOLOv5s[J]. Laser & Optoelectronics Progress, 2023, 60(10): 1010010

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