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Aiming at the problems that the current brightness detection methods of airport runway edge lights is relatively traditional which has low detection accuracy and their detection speed cannot meet the requirements of technical standards, an online detection method for brightness of runway edge lights based on improved CenterNet is proposed, and key points are used to achieve classification and boundary regression. Firstly, ResNeXt is used to replace the original ResNet backbone network of the model to improve the image feature extraction ability of runway edge lights. Secondly, to address the problems that the runway edge light target is small and easy to be confused with other lights in the flight area, a feature fusion network is designed to enhance the feature expression ability of runway edge light spots. Finally, depthwise separable convolution is introduced to reduce the amount of network parameters. Experiments are carried out on the self-built runway edge lights dataset, and the results show that the proposed runway edge lights brightness detection method has strong robustness under different weather backgrounds, and the average accuracy reaches 97.09%, which increased by 2.38% and solves the problems of inaccurate positioning, multi-checks, and false detection in the original model.
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