P-CenterNet for chimney detection in optical remote-sensing images

doi:10.11996/JG.j.2095-302X.2023020233

Abstract

Abstract:

Industrial chimney emissions are among the primary drivers of urban air pollution, with the urban environment quality inversely related to the quantity of chimneys therein. Therefore, the detection of chimney placement exerts a positive impact on urban environmental detection and governance. To address the problem of low detection accuracy caused by complex backgrounds and small targets in optical remote sensing images with numerous similar objects, P-CenterNet, a CenterNet-based detector, has been proposed for chimney detection tasks. Firstly, P-CenterNet employed pyramidal convolution in the backbone network to obtain richer linguistic features, instead of normal convolution in the backbone network. Secondly, a multi-scale contextual feature extraction module was designed in parallel with the backbone network to retain low-level feature information that helped distinguish object regions from background regions. Finally, a convolutional block attention module was added to further extract the output features of the backbone network to improve the detector′s representation of small targets. Furthermore, DIOR, a large-scale public dataset, was applied for the validation of the model in the experiments. The dataset was expanded and the robustness of the model was enhanced via both online and offline enhancements. The results indicated that P-CentreNet could significantly improve detection accuracy with a similar detection time cost, compared with other models such as Faster-RCNN and YOLOv3, with mAP reaching 89.77%.

Key words: chimney detection, optical remote sensing images, CenterNet, pyramidal convolution, attention mechanism

CLC Number:

TP391

XIE Guo-bo, HE Di-xuan, HE Yu-qin, LIN Zhi-yi. P-CenterNet for chimney detection in optical remote-sensing images[J]. Journal of Graphics, 2023, 44(2): 233-240.

Figures/Tables 9

References 25

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参数	值
种类	20
分辨率	0.5~30.0 m
图像数量	23 463
包含烟囱目标的图像数量	854
烟囱实例	1 696
像素	800×800 pixels
年份	2018

参数	值
种类	20
分辨率	0.5~30.0 m
图像数量	23 463
包含烟囱目标的图像数量	854
烟囱实例	1 696
像素	800×800 pixels
年份	2018

模型	Backbone	评价指标
模型	Backbone	P (%)	R (%)	mAP0.75 (%)	FPS
Faster-RCNN	ResNet50	66.81	77.11	72.30	8.64
Yolov3	DarkNet53	83.95	81.93	77.81	24.52
CenterNet (baseline)	ResNet50	88.42	80.96	75.99	49.92
CenterNet	PvConv-ResNet50	88.63	82.65	80.80	28.26
P-CenterNet	PvConv-ResNet50	93.91	89.16	89.77	28.21

模型	Backbone	评价指标
模型	Backbone	P (%)	R (%)	mAP0.75 (%)	FPS
Faster-RCNN	ResNet50	66.81	77.11	72.30	8.64
Yolov3	DarkNet53	83.95	81.93	77.81	24.52
CenterNet (baseline)	ResNet50	88.42	80.96	75.99	49.92
CenterNet	PvConv-ResNet50	88.63	82.65	80.80	28.26
P-CenterNet	PvConv-ResNet50	93.91	89.16	89.77	28.21

PyConv	MSCF	CBAM	评价指标
PyConv	MSCF	CBAM	P (%)	R (%)	mAP0.75 (%)	FPS	Params size (MB)
N	N	N	88.42	80.96	75.99	49.92	124.61
Y	N	N	88.63	82.65	80.80	28.69	121.90
Y	Y	N	89.11	84.82	82.61	28.61	121.96
Y	N	Y	93.89	88.92	88.93	28.56	125.92
Y	Y	Y	93.91	89.16	89.77	28.21	125.97