一种改进CenterNet的轻量化目标检测算法

doi:10.19665/j.issn1001-2400.2022.05.016

Abstract

Abstract:

Due to the complex structure,there are a large number of parameters in the CenterNet,which leads to a high computational complexity and long inference time.To solve this problem,a CenterNet-encoder algorithm is proposed for lightweight object detection.First,the fire module is used in the backbone to reduce the number of parameters and increase the calculation speed.Then,an encoding layer is utilized between the backbone and the head,which can increase the receptive field and obtain more accurate corners and center points for heatmaps.Finally,MSE loss is employed in bounding box regression,which accelerates the convergence and further improves the performance.The proposed algorithm achieves 40.5 AP on the MS-COCO test-dev benchmark with 47M parameters.Under the AMD5900X\32GB\RTX3090 environment configuration,the detection speed reaches 18FPS.Experimental results show that the performance of the proposed method is better than other lightweight methods in the number of parameters,inference time and detection accuracy.Although the precision of the proposed method is slightly lower than that of the CenterNet,the number of parameters is reduced by 77.6%,and the inference speed is increased by 69.3%.

Key words: fire module, lightweight, encoder, object detection

CLC Number:

TP391

LI Yueyan, CHENG Peitao, DU Shuxing. Lightweight object detection algorithm based on the improved CenterNet[J].Journal of Xidian University, 2022, 49(5): 137-144.

Figures/Tables 10

References 23

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算法	Backbone	参数量	速度/(ms·image^-1)	AP	AP₅₀	AP₇₅	AP_S	AP_M	AP_L
YOLOv3	DarkNet-53	61.9×10⁶	51	33.0	57.9	34.4	18.3	35.4	41.9
CornerNet_Squeeze	Hourglass-104	31.7×10⁶	33	34.4	49.2	36.2	14.8	36.9	49.5
CenterNet-encoder	Hourglass-104	47.0×10⁶	57	40.5	58.1	42.8	20.5	44.1	56.0

算法	Backbone	参数量	速度/(ms·image^-1)	AP	AP₅₀	AP₇₅	AP_S	AP_M	AP_L
CornerNet	Hourglass-52	99.0×10⁶	125	37.8	53.7	40.1	17.0	39.0	50.5
CornerNet	Hourglass-104	201.0×10⁶	153	40.5	56.4	43.1	19.4	42.7	53.9
CenterNet	Hourglass-52	104.8×10⁶	166	41.6	59.4	44.2	22.5	43.1	54.1
CenterNet	Hourglass-104	210.0×10³	186	44.9	62.4	48.1	25.6	47.4	57.4
CenterNet-encoder	Hourglass-104	47.0×10³	57	40.5	58.4	43.0	20.7	44.5	56.3

偏移损失函数	AP	AP₅₀	AP₇₅	AP_S	AP_M	AP_L
SmoothL₁loss	39.4	57.3	41.7	19.5	42.6	55.4
MSEloss	40.5	58.4	43.0	20.7	44.5	56.3

Lightweight object detection algorithm based on the improved CenterNet

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