Resource-Based Port Material Yard Detection with SPPA-Net
-
Published:2022-12-08
Issue:24
Volume:14
Page:16413
-
ISSN:2071-1050
-
Container-title:Sustainability
-
language:en
-
Short-container-title:Sustainability
Author:
Zhang Xiaoyong, Xu Rui, Lu Kaixuan, Hao Zhihang, Chen ZhengchaoORCID, Cai Mingyong
Abstract
Since the material yard is a crucial place for storing coal, ore, and other raw materials, accurate access to its location is of great significance to the construction of resource-based ports, environmental supervision, and investment and operating costs. Its extraction is difficult owing to its small size, variable shape, and dense distribution. In this paper, the SPPA-Net target detection network was proposed to extract the material yard. Firstly, a Dual-Channel-Spatial-Mix Block (DCSM-Block) was designed based on the Faster R-CNN framework to enhance the feature extraction ability of the location and spatial information of the material yard. Secondly, the Feature Pyramid Network (FPN) was introduced to improve the detection of material yards with different scales. Thirdly, a spatial pyramid pooling self-attention module (SPP-SA) was established to increase the global semantic information between material yards and curtail false detection and missed detection. Finally, the domestic GF-2 satellite data was adopted to conduct extraction experiments on the material yard of the port. The results demonstrated that the detection accuracy of the material yard reached 88.7% when the recall rate was 90.1%. Therefore, this study provided a new method for the supervision and environmental supervision of resource-based port material yards.
Funder
The National Key Research and Development Program of China
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference30 articles.
1. Zhang, Q., Wang, S., and Zhen, L. (2022). Yard truck retrofitting and deployment for hazardous material transportation in green ports. Ann. Oper. Res., in press. 2. The Action Plan for Further Promoting Green Port Construction;Xi;China Logist. Purch.,2018 3. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11–14). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands. 4. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27–30). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA. 5. Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|