采用神经网络架构搜索的高分辨率遥感影像目标检测

doi:10.13229/j.cnki.jdxbgxb.20221472

Abstract

Abstract:

Aiming at the problems of traditional object detection methods on remote sensing images based on deep learning networks need hand-crafted architectures， which are overly dependent on expert experience and time-consuming， an object detection method for remote sensing images based on neural architecture search is proposed. The network is automatically built by pathwise sampling and evolutionary search strategy for object detection of remote sensing images. Experiments on DIOR dataset and RSOD dataset show that the mean average precision of object detection reached 67.8% and 85.5%， and the FLOPs are 208.47 G and 201.67 G， which are better than the network models such as Faster R-CNN， RetinaNet， NAS-FCOS， ResNet Strikes Back， HRNet and GRoIE in terms of detection accuracy and computational efficiency. The proposed method can automatically search the network architecture for object detection of high-resolution remote sensing images， which is superior to the hand-crafted classical networks.

Key words: remote sensing, high-resolution remote sensing image, network architecture search, object detection, pathwise sampling

CLC Number:

TP751.1

Jun YANG,Peng-fei HAN. Object detection of high-resolution remote sensing images by neural architecture search[J].Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2646-2657.

Figures/Tables 13

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网络	mAP	AP₅₀	AP₇₅	AP_s	AP_m	AP_l
Faster R-CNN	66.1	88.8	74.2	12.2	50.8	76.2
GRoIE	64.1	91.2	72.7	26.0	57.3	71.4
HRNet	63.3	91.1	72.7	28.5	52.0	70.8
ResNet-SB	52.0	76.4	57.5	9.7	31.2	62.1
RetinaNet	34.3	55.1	36.7	3.9	19.4	42.8
NAS-FCOS	55.7	80.4	60.6	10.4	35.8	65.5
本文	67.8	91.3	78.3	36.1	60.6	74.4

类别	Faster R-CNN	GRoIE	HRNet	ResNet-SB	RetinaNet	NAS-FCOS	本文
机场	66.0	60.0	63.4	42.2	10.9	52.2	58.2
车辆	46.7	52.3	51.9	42.0	30.0	47.0	53.0
篮球场	81.0	77.9	72.3	63.9	50.1	68.7	81.9
田径场	82.2	78.1	80.2	65.6	46.9	65.5	81.4
风车	56.1	54.0	47.5	43.2	30.5	43.3	56.5
船舶	47.0	48.5	52.0	43.1	31.6	48.6	48.7
高速公路收费站	74.3	72.0	72.0	63.3	50.5	57.4	76.4
网球场	90.4	86.2	78.4	79.9	75.4	82.0	89.5
高尔夫球场	61.8	53.6	61.0	43.9	32.2	56.3	62.2
立交桥	57.0	59.0	57.1	44.3	22.2	38.9	61.9
储油罐	67.3	70.6	67.1	62.5	54.1	71.3	71.0
棒球场	86.5	85.2	84.7	77.6	75.9	83.5	85.4
港口	41.7	34.3	44.3	26.3	8.4	34.0	51.4
体育场	77.9	76.6	75.9	69.2	47.0	67.9	81.2
桥梁	43.7	49.4	45.4	31.8	10.5	27.5	55.3
飞机	81.5	78.0	74.9	67.8	59.9	75.5	80.6
火车站	49.2	48.9	44.2	19.2	5.2	25.4	52.1
高速公路服务区	70.1	62.8	63.7	44.0	9.6	53.1	68.7
水坝	55.0	52.8	48.4	36.5	19.3	35.4	58.5
烟囱	81.7	81.3	81.6	74.7	63.2	80.0	82.0

网络	FLOPs/G	参数/M	搜索时间/h	训练时间/h	mAP
Faster R-CNN	798.04	32.95	—	20.6	66.1
GRoIE	545.00	43.12	—	22.1	64.1
HRNet	298.65	46.97	—	21.1	63.3
ResNet-SB	216.40	41.22	—	19.3	52.0
RetinaNet	285.98	20.01	—	17.1	34.3
NAS-FCOS	230.62	38.15	11.4	18.4	55.7
本文	208.47	25.38	12.6	20.8	67.8

网络	mAP	AP₅₀	AP₇₅	AP_s	AP_m	AP_l
Faster R-CNN	84.9	99.5	96.4	61.0	83.0	88.0
GRoIE	84.5	99.4	96.3	60.7	82.2	87.9
HRNet	84.1	99.2	97.3	59.9	82.5	87.2
ResNet-SB	77.4	97.5	90.5	23.3	66.4	82.3
RetinaNet	51.4	84.8	55.8	33.6	52.5	54.0
NAS-FCOS	77.3	98.0	89.2	26.9	75.0	83.1
本文	85.5	99.5	97.9	63.2	83.2	88.6

网络	飞机	操场	立交桥	油罐
Faster R-CNN	78.7	92.2	81.2	87.6
GRoIE	79.1	90.3	81.0	87.5
HRNet	78.5	89.8	81.4	86.9
ResNet-SB	60.3	93.1	81.4	74.8
RetinaNet	59.0	59.3	30.1	57.1
NAS-FCOS	66.3	86.8	72.6	83.5
本文	79.9	90.0	85.2	87.9

Object detection of high-resolution remote sensing images by neural architecture search

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