Application of Improved Instance Segmentation Algorithm Based on VoVNet-v2 in Open-Pit Mines Remote Sensing Pre-Survey

The traditional mine remote sensing information pre-survey is mainly based on manual interpretation, and interpreters delineate the mine boundary shape. This work is difficult and susceptible to subjective judgment due to the large differences in the characteristics of mining complex within individuals and small differences between individuals. CondInst-VoV and BlendMask-VoV, based on VoVNet-v2, are two improved instance segmentation models proposed to improve the efficiency of mine remote sensing pre-survey and minimize labor expenses. In Hubei Province, China, Gaofen satellite fusion images, true-color satellite images, false-color satellite images, and Tianditu images are gathered to create a Key Open-pit Mine Acquisition Areas (KOMMA) dataset to assess the efficacy of mine detection models. In addition, regional detection was carried out in Daye Town. The result shows that the performance of improved models on the KOMMA dataset exceeds the baseline as well as the verification accuracy of manual interpretation in regional mine detection tasks. In addition, CondInst-VoV has the best performance on Tianditu image, reaching 88.816% in positioning recall and 98.038% in segmentation accuracy.

Automated summary

The traditional manual interpretation method for mine remote sensing pre-survey is subjective and time-consuming. To improve the efficiency and reduce labor costs, two improved instance segmentation models are proposed. The evaluation on different satellite image datasets shows that the improved models outperform the traditional method in mine detection tasks.