Intelligent Domestic Garbage Recognition Based on Faster RCNN

Dataset	Metal	Plastic	Carton	Battery	Bulb	Pill	Total
Original dataset	1321	1392	807	1058	1129	1402	7109
Augmented train dataset	2429	2435	1938	2389	2398	2528	14117
Augmented test dataset	630	585	483	597	617	630	3542
Augmented dataset	3059	3020	2421	2986	3015	3158	17659

Table 2. Number of parameters, FLOPs and layers for different networks
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Table 2. Number of parameters, FLOPs and layers for different networks
Network Number ofparameters /10⁷ Number ofFLOPs /10¹⁰ Layernumbers
VGG-16 136.79 166.37 20
Res101 47.21 167.25 105
MobileNet_v1 5.61 19.02 32

Table 3. Network results on train dataset (TR) and test dataset (TE)

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Table 3. Network results on train dataset (TR) and test dataset (TE)

Backbonenetwork		AP						mAP	OptimizedmAP	Detection speed /(frame·s^-1)
Backbonenetwork		Metal	Plastic	Carton	Battery	Pill	Bulb	mAP	OptimizedmAP	Detection speed /(frame·s^-1)
Res101	TR	1.0	0.9996	0.9985	0.9996	0.9973	1.0	0.9992	0.9993	~7
	TE	0.9770	0.9597	0.9817	0.9695	0.9728	0.9811	0.9736	0.9857
VGG-16	TR	1.0	0.9997	0.9997	0.9996	0.9970	1.0	0.9993	0.9992	~9
	TE	0.9758	0.9639	0.9866	0.9835	0.9813	0.9853	0.9794	0.9923
MobileNet_v1	TR	0.9817	0.9715	0.9732	0.9851	0.9831	0.9879	0.9804	0.9833	~20
	TE	0.9139	0.8737	0.9204	0.9408	0.9671	0.9554	0.9285	0.9490

Table 4. Test results on background dataset

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Table 4. Test results on background dataset

Backbone network	Original mAP	Status	mAP under different background types
Backbone network	Original mAP	Status	Pure color	Texture	Garbage
Res101	1.0	Before re-training	0.9913	0.9222	0.9050
		After re-training	1.0	1.0	1.0
VGG-16	1.0	Before re-training	0.9901	0.8835	0.6494
		After re-training	1.0	1.0	1.0
MobileNet_v1	0.9917	Before re-training	0.9691	0.7433	0.4204
		After re-training	0.9999	0.9933	0.9793

Table 5. Precision and recall on test dataset under optimal threshold of each network

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Table 5. Precision and recall on test dataset under optimal threshold of each network

Backbone network(P₁,P₂)	Parameter	Recyclable garbage				Hazardous garbage
Backbone network(P₁,P₂)	Parameter	Metal	Plastic	Carton	Mean	Battery	Pill	Bulb	Mean
Res101(0.76, 0.24)	Precision	0.9796	0.9662	0.9834	0.9764	0.9497	0.9792	0.9698	0.9662
	Recall	0.9889	0.9778	0.9834	0.9834	0.9799	0.9714	0.9887	0.9800
VGG-16(0.62, 0.38)	Precision	0.9583	0.9487	0.9775	0.9615	0.9657	0.9658	0.9534	0.9491
	Recall	0.9857	0.9795	0.9876	0.9843	0.9899	0.9873	0.9951	0.9908
MobileNet_v1(0.56, 0.44)	Precision	0.8943	0.9235	0.9109	0.9096	0.9266	0.9636	0.8867	0.9256
	Recall	0.9609	0.9322	0.9654	0.9528	0.9728	0.9739	0.9854	0.9774

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Canhua Wen, Jia Li, Xue Dong. Intelligent Domestic Garbage Recognition Based on Faster RCNN[J]. Laser & Optoelectronics Progress, 2020, 57(20): 201014

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