High-consequence area indentation of remote sensing images of China-Myanmar oil and gas pipeline based on improved YOLO model

doi:10.16265/j.cnki.issn1003-3033.2025.01.0632

Abstract

Abstract:

An improved object detection algorithm for high-consequence areas was proposed to solve the problems of the sensitive and complex external environment of the overseas section of the China-Myanmar oil and gas pipeline, difficulty in manual inspection, and high-risk factors. Firstly, a convolutional block attention module was used to adaptively learn channel and spatial attention to enhance the network's perception and generalization capabilities. Then, focal and efficient intersection over union(Focal-EIoU) loss was used to comprehensively consider the target features and their associations to deal with the issues of class imbalance, reduce the interference of easy-to-classify samples, and enhance the robustness of the model. Finally, the improved model was used to intelligently recognize regional attributes of China-Myanmar oil and gas pipeline remote sensing images. Furthermore, the proposed YOLO model was validated against related ablation experiments. The results showed that for the feature recognition of remote sensing images of the China-Myanmar oil and gas pipeline, the proposed model reached a mean average precision (mAP) of 68.2% for the field, green space, settlement, and river. The model performance was improved by 29%, 21.6%, and 10.7% compared with YOLOv5, YOLOx, and YOLOv8, respectively.

Key words: YOLO, China-Myanmar oil and gas pipeline, remote sensing images, high consequence areas, object detection, intelligent identification

CLC Number:

X924.2安全监测技术与设备

JIANG Yuanliang, REN Qingying, REN Yuan, LIU Haipeng, DONG Shaohua. High-consequence area indentation of remote sensing images of China-Myanmar oil and gas pipeline based on improved YOLO model[J]. China Safety Science Journal, 2025, 35(1): 103-111.

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模型	AP/%				mAP@0.5/%	mAP@0.5:0.95/%
模型	田地	绿地	居住地	河流	mAP@0.5/%	mAP@0.5:0.95/%
YOLOv5	49.2	52.1	54.5	1.0	39.2	22.8
YOLOx	59.8	59.1	49.5	17.8	46.6	21.5
YOLOv8	53.5	59.3	58.3	59.1	57.5	24.4
改进模型	62.7	57.8	52.8	99.5	68.2	30.0

序号	注意力机制	损失函数			mAP/%
序号	注意力机制	CIoU	EIoU	Focal	mAP/%
1	未添加CBAM	√	—	—	57.5
2		—	√	—	58.7
3		—	√	√	61.2
4	添加CBAM	√	—	—	59.7
5		—	√	—	67.1
6		—	√	√	68.2