Abstract: A method of training a learning machine to detect spills of hydrocarbon liquids from pipelines. A neural network is trained by collecting samples of a number of different ground materials as well as a number of liquid hydrocarbons. For each hydrocarbon, a spill is simulated on each ground material. For each of these spills, a thermal camera and a visible light camera are used to capture images. The images from the two cameras are fused, and input to the neural network for classification training. Once the neural network is trained, a system having the two cameras and the neural network can be used to detect actual hydrocarbon spills.

Abstract: A method of training a learning machine to detect spills of hydrocarbon liquids from pipelines. A neural network is trained by collecting samples of a number of different ground materials as well as a number of liquid hydrocarbons. For each hydrocarbon, a spill is simulated on each ground material. For each of these spills, a thermal camera and a visible light camera are used to capture images. The images from the two cameras are fused, and input to the neural network for classification training. Once the neural network is trained, a system having the two cameras and the neural network can be used to detect actual hydrocarbon spills.