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In recent decades, the use of optical detection systems for meteor studieshas increased dramatically, resulting in huge amounts of data being analyzed.Automated meteor detection tools are essential for studying the continuousmeteoroid incoming flux, recovering fresh meteorites, and achieving a betterunderstanding of our Solar System. Concerning meteor detection, distinguishingfalse positives between meteor and non-meteor images has traditionally beenperformed by hand, which is significantly time-consuming. To address thisissue, we developed a fully automated pipeline that uses Convolutional NeuralNetworks (CNNs) to classify candidate meteor detections. Our new method is ableto detect meteors even in images that contain static elements such as clouds,the Moon, and buildings. To accurately locate the meteor within each frame, weemploy the Gradient-weighted Class Activation Mapping (Grad-CAM) technique.This method facilitates the identification of the region of interest bymultiplying the activations from the last convolutional layer with the averageof the gradients across the feature map of that layer. By combining thesefindings with the activation map derived from the first convolutional layer, weeffectively pinpoint the most probable pixel location of the meteor. We trainedand evaluated our model on a large dataset collected by the Spanish MeteorNetwork (SPMN) and achieved a precision of 98\%. Our new methodology presentedhere has the potential to reduce the workload of meteor scientists and stationoperators and improve the accuracy of meteor tracking and classification.

Deep machine learning for meteor monitoring: advances with transfer learning and gradient-weighted class activation mapping