I-Corps: Delivering Integrated Technology for Automated Wide-Area Humanitarian Mine Action Surveys

I-Corps：为自动化广域人道主义排雷行动调查提供集成技术

• 批准号: 2313759
• 负责人: Alex Nikulin
• 金额: $ 5万
• 依托单位: SUNY at Binghamton
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2024-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2313759&HistoricalAwards=false
• 关键词: Corps; Delivering; Integrated; Technology; Automated

The broader impact/commercial potential of this I-Corps project is the development of technologies that effectively detect and clear landmines over wide surface areas. Humanitarian demining is the process where agencies (governmental or otherwise) survey a potentially hazardous area to detect, identify, and subsequently clear any explosive hazards (landmines or unexploded ordinance) in a post-conflict region. The goal of humanitarian demining is to release previously explosive-contaminated land back to impacted communities to allow the land to be used and developed without fear of injury and death caused by landmines and unexploded ordnance. The enormous variety of explosives, environments in which they lie, and conditions in which they may be found, create a multi-faceted problem that requires innovative technological solutions. This project is a demonstration of the powerful and wide-reaching capabilities of drones and machine learning in humanitarian mine action applications. The implementation of this technology will dramatically increase the rate and safety of demining, increasing the safety of demining operators, and allowing communities to reclaim previously inaccessible land for future development.This I-Corps project is based on the development of an integrated artificial intelligence-assisted unmanned aerial vehicle drone survey platform that a demining agency could quickly and effectively use to perform initial wide-area surveys. This technology will save money and time on subsequent demining activities in areas of critical contamination. The methods can be expanded to detect not only direct evidence of landmines and unexploded ordinances, but also indirect evidence such as explosion craters, minefield warning signs, or dead livestock potentially killed by explosives. Combining state-of-the-art deep learning technology, commercially available drones, and miniaturized optical sensors, this team created new solutions. This technology involves drone survey methods combined with innovative deep learning algorithms. The first step consists of integrating commercial visual light, thermal and multispectral drones to survey areas that are suspected to be contaminated with explosives. After the survey is complete, the deep learning method is used to generate the predicted coordinate locations of the explosives. In addition, a high-quality map of the suspected hazardous region is produced with each predicted explosive marked and labeled with its predicted class. The entire process takes place without using the internet, is user friendly, and can detect and classify most of the aerially-visible landmines and unexploded ordinances at a rate of 800 square meters per hour.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

I-Corps项目的更广泛影响/商业潜力是开发有效探测和清除大面积地雷的技术。人道主义排雷是指各机构（政府机构或其他机构）对潜在危险区域进行调查，以探测、识别并随后清除冲突后地区的任何爆炸危险（地雷或未爆弹药）。人道主义排雷的目标是将以前受爆炸物污染的土地归还给受影响的社区，使土地能够在不用担心地雷和未爆弹药造成伤亡的情况下使用和开发。爆炸物种类繁多，所处的环境和可能被发现的条件造成了一个多方面的问题，需要创新的技术解决办法。该项目展示了无人机和机器学习在人道主义排雷行动应用中的强大和广泛的能力。该技术的实施将大大提高排雷的速度和安全性，提高排雷操作人员的安全性，并使社区能够开垦以前无法进入的土地，以供未来开发。I-Corps项目基于开发一个集成的人工智能辅助无人驾驶飞行器无人机调查平台，排雷机构可以快速有效地使用该平台进行初步的大面积调查。 这一技术将为严重污染地区的后续排雷活动节省资金和时间。这些方法可以扩展到不仅探测地雷和未爆炸弹药的直接证据，而且还探测爆炸坑、雷区警告标志或可能被爆炸物炸死的牲畜尸体等间接证据。结合最先进的深度学习技术、商用无人机和微型光学传感器，该团队创造了新的解决方案。这项技术涉及无人机调查方法与创新的深度学习算法相结合。第一步包括整合商业可见光、热和多光谱无人机，以调查怀疑被爆炸物污染的地区。调查完成后，使用深度学习方法来生成爆炸物的预测坐标位置。此外，还绘制了疑似危险区域的高质量地图，并对每种预测的爆炸物进行标记，标明其预测类别。整个过程无需使用互联网，用户友好，可以以每小时800平方米的速度检测和分类大多数空中可见的地雷和未爆炸的弹药。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。