SPRING POINT DETECTION OF HIGH RESOLUTION IMAGE BASED ON YOLOV3

Abstract. The Xinjiang region of China is a vast and sparsely populated area with complex topography, surrounded by basins and mountains, and its geomorphological features and water circulation process make the traditional spring water resource acquisition time-consuming, labor-consuming and inaccurate. Remote Sensing Technology has the advantages of large scale, periodicity, timeliness and comprehensiveness in target detection. In order to realize the artificial intelligence detection of springs in Xinjiang, this paper presents a method of detecting springs in remote sensing image based on the YOLOV3 network framework, based on the data set of 512 * 512 by using 0.8m remote sensing image annotation, a model of recognition of spring point based on Yolov3 network is constructed and trained. The results show that the map of spring point is 0.973, which is the basis of monitoring and protecting the natural environment in the Belt and Road Initiatives.

[1]  Kazuya Kaku,et al.  Satellite remote sensing for disaster management support: A holistic and staged approach based on case studies in Sentinel Asia , 2019, International Journal of Disaster Risk Reduction.

[2]  Shenghua Zhou,et al.  Persymmetric adaptive detection of distributed targets in compound-Gaussian sea clutter with Gamma texture , 2018, Signal Process..

[3]  S. Fabre,et al.  Monitoring oil contamination in vegetated areas with optical remote sensing: A comprehensive review. , 2020, Journal of hazardous materials.

[4]  Yanbo Huang,et al.  Agricultural remote sensing big data: Management and applications , 2018, Journal of Integrative Agriculture.

[5]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Joon‐Soo Lee,et al.  Red tide detection using deep learning and high-spatial resolution optical satellite imagery , 2020, International Journal of Remote Sensing.

[7]  Dongjian He,et al.  FLYOLOv3 deep learning for key parts of dairy cow body detection , 2019, Comput. Electron. Agric..

[8]  Jian Xue,et al.  Adaptive subspace detection of range-spread target in compound Gaussian clutter with inverse Gaussian texture , 2018, Digit. Signal Process..

[9]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[10]  Joseph Redmon,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[11]  Jun Du,et al.  Real-time unmanned aerial vehicle tracking of fast moving small target on ground , 2018, J. Electronic Imaging.

[12]  Qing Sun,et al.  Research on the multi-scale low rank method and its optimal parameters selection strategy for infrared small target detection , 2019, Optik.

[13]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[15]  Zhang Yi,et al.  An improved tiny-yolov3 pedestrian detection algorithm , 2019, Optik.

[16]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.