<![CDATA[Bad weather often interferes with the functioning of the air transport system. One example is the frequent flight delays for commercial aircraft, resulting in losses for both the airline and passengers. Artificial Intelligence (AI) technology can now minimize delays caused by bad weather, especially in low visibility conditions. This paper discusses AI modeling that can detect aircraft in a low visibility weather condition, especially in the airport area. The employed method is the deep learning approach with the YOLOv4 algorithm (single-stage detection), which is regarded as one of the optimal platforms in this field. There are 600 images used in this work to create and train three different models. Image Dehazing filter is employed on the training data before it is trained to produce the detection model. The result shows that the model has a good performance in terms of performance metrices. Thus, this model is suitable to be used to detect aircraft in low visibility conditions.]]>
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