A Machine-learning, artificial-intelligence system designed to flag safety hazards on construction sites has once again provided a backstop for the judges of新利18备用ENR的年度摄影比赛。该软件被称为“ Vinnie”（用于洞察力和评估的非常智能的神经网络），该软件使用与自动驾驶汽车中类似的对象识别技术来分析施工照片和视频的风险。该系统两年前由位于马萨诸塞州剑桥市的Smartvid.io Inc.开发，将图像中的像素与对象库匹配，以自动标记或注意没有特定物品，例如硬汉，手套或安全颜色。
In 2016, ENR and several industry firms contributed thousands of construction images to help train and test VINNIE. The system processed unidentified images from ENR’s photo-contest database to help launch the software. The product’s basic level of hazard-spotting intelligence also was introduced as a free public utility in 2017.
VINNIE’s accuracy and capabilities have improved since it was deployed in ENR’s 2016 Year in Construction Photo Contest. In addition to scanning for hardhats and safety colors, VINNIE now sees gloves and eyewear. After looking at 763 images submitted to this year’s contest, VINNIE found 97 people not wearing gloves, 31 without hardhats, 89 without safety colors and 11 without eyewear. In each case it is up to human experts to decide whether those instances depict actionable risk.
VINNIE flagged two photos chosen by ENR’s human judges as finalists. One features a gloveless worker whose hands are partially obscured. The other shows two ironworkers without high-visibility vests. ENR decided to publish both photos after conferring with this year’s contest safety judge, Keith Snead, safety director at Limback Holdings Inc. Snead says that while the workers “may be violating site-specific requirements set by general contractors,” they aren’t violating Occupational Safety Health Administration regulations.
Josh Kanner, Smartvid.io’s founder and CEO, says detecting gloves is “an important addition,” because several of his customers have adopted 100% glove compliance policies “due to the high prevalence and cost of hand lacerations.” Hand injuries are one of the leading nonfatal injuries involving days away from work in private industry, according to the U.S. Bureau of Labor Statistics.
位于波士顿的联系人萨福克（Suffolk）（在其许多项目上都使用SmartVid.io，大约在18个月前就需要手套。新利18备用网址萨福克地区安全经理马蒂·莱克（Marty Leik）表示，Vinnie帮助提高了分包商之间的个人保护设备（PPE）合规性率。莱克（Leik）曾帮助Enr的2016年摄影大新利18备用赛（Enr）审判，他使用Vinnie来确定特定的工作场所和不符合PPE的任何交易。莱克说：“这使我们能够与贸易伙伴交往并跟进他们实施改进。”他强调说，Vinnie不习惯惩罚个人，而是“严格用于改进和现场每个人的好处”。
Leik希望Vinnie认识到安全带和系束带，以及秋天的暴露，例如开孔或缺少护栏，Kanner说，发现更具挑战性。坎纳说：“您需要知道是否有优势，然后警报缺少护栏，而这只是两列之间什么都没有的列。”“像缺少护栏的两列之间的跨度一样，需要大量上下文。”
坎纳说，Vinnie将能够在第1季度结束时识别出滑移，跳闸和掉落危险，例如碎屑和无序材料。
但是，由于“凌乱的工作人员”的定义是“模棱两可的”，坎纳说，Vinnie不仅会因为在地面上有一定数量的错误咖啡杯而旗帜。坎纳说，Vinnie正在学习“查看图像中的所有内容并自行决定”，如果该地区构成威胁。
SmartVid.io在2017年期间还与其他几个系统集成在一起，例如Autodesk的BIM 360字段。与Oxblue的施工延时摄像机的集成最近上线了，将在第1季度后期正式宣布。坎纳说：“该集成需要不到90秒才能设置，然后数据开始从网站上的一个或多个oxblue摄像机流入Smartvid.io平台。”
公司开始使用Vinnie而不仅仅是安全。Clayco Inc.正在通过将上传到SmartVid.io的图像与Jobsite上的实际图像进行比较来简化进度跟踪。现场中的语音标签有助于自动将SmartVID.IO应用中的内容与用于语音分析的机器学习的特定关键字对齐。
Clayco还根据内容类别（例如“倾斜构造”）来编目多年的成像数据，因此可以轻松地召回营销和其他目的，而不是将这些内容撒在服务器上，并希望有一天有人能够有一天能够find it,” says Tomislav Žigo, Clayco’s vice president for virtual design and construction.
Žigo最终会想象使用Vinnie识别建筑组件，例如混凝土和钢筋，并通过分析潜在的现场危害和出口路线来优化现场物流。也许Vinnie甚至可以以可能造成伤害的方式来标记工人使用工具。
“Anything that can be automatically recognized and tagged,” Žigo says, “we find useful.”
Related:ENR's "2017 Year in Construction" Photo Contest Winners
