Abstract
Images of the pipeline can be easily captured by the infrared thermal camera. However, factors such as uneven lighting or the complex and diverse outdoor environment can affect the pipeline infrared image. Under these circumstances, detection of the abnormally high-temperature areas of pipe insulation becomes very difficult because the contrast between the image target and background is reduced. This work aims to overcome these issues and presents an immune neural network algorithm. The existing artificial immune algorithm only reflects the function and characteristics of adaptive immunity, without considering the holistic nature of the biological immune system. This method draws on the synergy mechanism of the innate immune network and the adaptive immune network in the biological immune system and combines the holistic immune system with the neural network. The method can effectively detect abnormally high-temperature areas in the pipeline infrared images caused by insulation faults and has higher detection accuracy compared with other image detection algorithms.
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