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When it comes to addressing the safety/security related needs at differentproduction/construction sites, accurate detection of the presence of workers,vehicles, equipment important and formed an integral part of computervision-based surveillance systems (CVSS). Traditional CVSS systems focus on theuse of different computer vision and pattern recognition algorithms overlyreliant on manual extraction of features and small datasets, limiting theirusage because of low accuracy, need for expert knowledge and high computationalcosts. The main objective of this paper is to provide decision makers at siteswith a practical yet comprehensive deep learning and IoT based solution totackle various computer vision related problems such as scene classification,object detection in scenes, semantic segmentation, scene captioning etc. Ouroverarching goal is to address the central question of What is happening atthis site and where is it happening in an automated fashion minimizing the needfor human resources dedicated to surveillance. We developed Deep ExxonMobil Eyefor Video Analysis (DEEVA) package to handle scene classification, objectdetection, semantic segmentation and captioning of scenes in a hierarchicalapproach. The results reveal that transfer learning with the RetinaNet objectdetector is able to detect the presence of workers, different types ofvehicles/construction equipment, safety related objects at a high level ofaccuracy (above 90%). With the help of deep learning to automatically extractfeatures and IoT technology to automatic capture, transfer and process vastamount of realtime images, this framework is an important step towards thedevelopment of intelligent surveillance systems aimed at addressing myriads ofopen ended problems in the realm of security/safety monitoring, productivityassessments and future decision making.

DEEVA: A Deep Learning and IoT Based Computer Vision System to Address Safety and Security of Production Sites in Energy Industry