  In recent decades, the use of optical detection systems for meteor studies
has increased dramatically, resulting in huge amounts of data being analyzed.
Automated meteor detection tools are essential for studying the continuous
meteoroid incoming flux, recovering fresh meteorites, and achieving a better
understanding of our Solar System. Concerning meteor detection, distinguishing
false positives between meteor and non-meteor images has traditionally been
performed by hand, which is significantly time-consuming. To address this
issue, we developed a fully automated pipeline that uses Convolutional Neural
Networks (CNNs) to classify candidate meteor detections. Our new method is able
to detect meteors even in images that contain static elements such as clouds,
the Moon, and buildings. To accurately locate the meteor within each frame, we
employ the Gradient-weighted Class Activation Mapping (Grad-CAM) technique.
This method facilitates the identification of the region of interest by
multiplying the activations from the last convolutional layer with the average
of the gradients across the feature map of that layer. By combining these
findings with the activation map derived from the first convolutional layer, we
effectively pinpoint the most probable pixel location of the meteor. We trained
and evaluated our model on a large dataset collected by the Spanish Meteor
Network (SPMN) and achieved a precision of 98\%. Our new methodology presented
here has the potential to reduce the workload of meteor scientists and station
operators and improve the accuracy of meteor tracking and classification.
