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.

翻译：近几十年来，用于流星研究的光学探测系统使用量急剧增加，导致需分析的数据量极为庞大。自动流星探测工具对于持续研究流星体入射通量、回收新鲜陨石以及加深对太阳系的理解至关重要。在流星探测方面，传统上需人工区分流星与非流星图像中的假阳性，这一过程耗时显著。为解决此问题，我们开发了一套全自动流水线，采用卷积神经网络（CNN）对候选流星探测结果进行分类。我们的新方法甚至能在包含云、月亮和建筑物等静态元素的图像中检测到流星。为精确定位每帧图像中的流星位置，我们采用了梯度加权类激活映射技术。该方法通过将最后一个卷积层的激活值与整个其特征图上梯度的平均值相乘，从而辅助识别感兴趣区域。结合这些结果与首个卷积层推导出的激活图，我们有效锁定了流星最可能的像素位置。我们利用西班牙流星网络（SPMN）收集的大型数据集对模型进行了训练与评估，达到了98%的精确率。这里提出的新方法有望减轻流星科学家与观测站操作员的工作负担，并提升流星追踪与分类的准确性。