Advanced science.  Applied technology.


Autonomous Aerial Detection of Hazardous Liquid Pipeline Leaks in Water Bodies, 10-R8862

Principal Investigators
Jake Janssen
Heath Spidle
Inclusive Dates 
07/01/19 - Current


The prevailing leak detection systems used today are unable to detect leaks that are smaller than 1% of the pipeline throughput. As such, many leaks go unnoticed until they reach a size large enough to be detected by CPM systems or until leak evidence is observed. Because of commercially available solutions for crude on water leak detection and the increasing pressure from regulating agencies for operators to improve their leak detection capabilities, there is an increased interest in developing technology to detect oil on water.

SwRI has developed the Smart LEak Detection (SLED) system to autonomously monitor pipelines for hazardous chemical spills. SLED leverages a visible and a thermal camera along with machine learning techniques to reliably detect the chemical fingerprint of small hazardous liquid leaks on surfaces typically found near pipelines. The foundations for SLED will be used to see if we can adapt this same approach to detect crude oil on the surface of water.


The goal of this project was to investigate using visible and thermal cameras to detect crude oil on water bodies. The first step of this project was to investigate the sensors to be used. The previous SLED project had already picked out a thermal and visible camera, used as a starting point for this project. The thermal camera used on SLED was used, but a higher resolution visible camera was used to increase resolution to give better detections.

The second step was to collect data of crude oil and non-crude oil materials that could fool the machine learning algorithm. This happened over three different collections. The first collection used four 4-foot by 4-foot pools of water with varying substrates to give different color contrasts. The second data collection used four 4-foot by 4-foot pools again, and a larger 10-foot diameter pool was used to provide more area for the oil to move. The last data collection used two 10-foot diameter pools and split the collection over two days. The first day consisted of negative training of leaves and black trash bags. The second day collected crude oil data with more variations in light, oil quantity, shadows, and types of oil than the two previous data collections.


The results of this project have shown favorable results that crude oil can be detected using visible and thermal cameras. The algorithm was able to achieve a 98% accuracy at detecting oil in pools used to collect data. This is promising, and with more diverse training data in different environments we are confident that our algorithm can be further improved.