Intelligent Vehicle Object Detection Algorithm Based on Lightweight CenterNet

doi:10.12141/j.issn.1000-565X.230513

Abstract

Abstract:

Aiming at the problem that the current object detection algorithm has many parameters and larger computation amount, resulting in slower response and difficulties of application in intelligent vehicle systems, this paper proposed an improved CenterNet object detection algorithm. That is, the lightweight MobileNetV3 network was applied to replace the original ResNet-50 network to reduce the amount of computation; the depth-separable PANet was applied to replace the feature enhancement network to obtain the features after the fusion of the multi-scale feature information, and the SimAM attention mechanism was introduced to strengthen the attention of the target features before the fusion of the features, then the SiLU activation function was used to replace the original object detection network in the ReLU activation function to enhance the network learning. Finally, the CPAN-ASFF module was proposed to fuse the depth-separable PANet output multi-scale feature maps to improve the object detection accuracy. The optimized KITTI dataset was applied to train and detect the improved CenterNet object detection algorithm for validation. And the results show that its mean average precision is 80.7%, which is 12 percentage points higher than that of the original CenterNet object detection algorithm; its detection speed is 65 f/s, and its number of parameters is 8.91 M, which is 72.73% less than the original algorithm. The improved algorithm performs better in the detection of occluded objects, overlapped objects and objects similar to the background. In the SODA10M dataset, the detection accuracy and speed of the algorithm are better than the current mainstream algorithms. The optimization of the algorithm and the experiments in the paper laid the technical support for the application of intelligent vehicles in practical engineering.

Key words: intelligent vehicles, object detection, anchor-free, CenterNet, lightweight, depthwise separable convolution

CLC Number:

TP391.4

YUE Yongheng, NING Ruihou. Intelligent Vehicle Object Detection Algorithm Based on Lightweight CenterNet[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(8): 45-55.

Figures/Tables 15

Fig.1

Fig.2

Table 1

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

Table 2

Ablation test"

方法	Mobile-netV3	FPN	CPAN-ASFF	SiLU	SimAM	$s A P, C / %$	$s A P, P / %$	参数/M	F/（f·s^-1）	$M A P / %$
1	×	×	×	×	×	78.9	58.5	32.67	68	68.7
2	√	×	×	×	×	78.6	53.6	4.24	82	66.1
3	√	√	×	×	×	84.2	59.6	5.16	76	71.9
4	√	√	√	×	×	89.8	67.6	8.91	66	78.7
5	√	√	√	√	×	90.0	69.0	8.91	66	79.5
6	√	√	√	√	√	90.6	70.9	8.91	65	80.7

Table 2

Fig.12

Table 3

Detection results of different models on the dataset"

网络模型	改进KITTI		SODA10M		参数/M
网络模型	$M A P / %$	$F / f · s - 1$	$M A P / %$	$F / f · s - 1$	参数/M
Faster-RCNN	74.3	30	55.7	28	137.10
YOLOv3	72.5	64	54.6	64	61.95
YOLOV4	76.1	52	57.1	50	64.36
YOLOV5x	77.6	44	59.3	43	87.78
CenterNet	68.7	68	53.8	67	32.67
文中提出的算法	80.7	65	63.3	64	8.91

Table 3

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Input	Module/numbers	K	SE	Activation
512×512×3	Conv2D/1	3	×	HS
256×256×16	Bneck/1	3	×	RE
256×256×16	Bneck/2	3	×	RE
128×128×24	Bneck/3	5	√	RE
64×64×40	Bneck/4	3	×	HS
32×32×80	Bneck/2	3	√	HS
32×32×112	Bneck/3	5	√	HS

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