Smart Autopilot Drone System for Surface Surveillance and Anomaly Detection via Customizable Deep Neural Network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Copter-based unmanned aerial vehicle (drone) systems are being utilized for surveillance, inspection and security purposes for well sites, gathering centers, pipelines, refineries, and other surface facilities. However, most of the practices largely rely on humans, including drone operation, data transfer, image analysis, etc. In this paper, we present a comprehensive, cloud-enabled, human-free autopilot drone system and its application in field surveillance and anomaly detection powered by customizable deep neural network and computer vision models. The proposed system consists of customized quadcopter drones equipped with high-definition cameras, thermal imaging and gas sensing devices, autopiloted by cloud-connected onboard computers. A series of advanced algorithms are developed and deployed onboard and over the Cloud for processing and diagnosing the image/thermal/gas sensing data collected by the drones in real-time or near real-time, including accurate 2D geospatial aerial mapping, anomaly detection and classification for events like oil leak, gas leak, facility failure, human activities, etc. Object detection deep learning models are customized and parallelized for low-profile multi-core single board computers. In our case study, a pre-configured drone flew along the same path twice at a 6-month gap. A robust, iterative image registering algorithm is developed to precisely align and overlay images taken at different days at the same or similar GPS locations, even with significant changes to the environment due to season shift, human activities, camera angles or height variations. Local changes are filtered and selected based on their sizes and magnitudes in the residual images by subtracting pairs of perfectly overlaid scenes. Pre-trained Residual Convolutional Neural network (He et al. 2015) is rapidly re-trained to further classify the type of changes using the techniques of transfer learning and data augmentation. An ROC of 99% was achieved in the multi-task binary classification, wherein the detected changes are divided into positive anomalies (such as oil/gas leak, facility failures, unauthored human activities) and negative (natural/insignificant) signals. Comparing against a support vector machine baseline with a ROC=92%, the ResNet model demonstrates significant, more promising detection accuracy at a faster training time. This innovative integrated platform is presented that combines physical drone, onboard imaging/sensing devices, cloud connectivity, onboard and back-end control system, deep learning and computer vision architecture for situational awareness of oil & gas fields and the mining industry. It achieves full automation of mass surveillance, data acquisition and storage, diagnostics and asset situational understanding. The system architecture, especially the onboard and cloud computation engines, can be readily transferred and applied to other common drone platforms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it