🤖 AI Summary
Manual verification in wide-field solar system surveys severely limits the efficiency of moving object detection. Method: This paper proposes an end-to-end multi-input convolutional neural network (CNN) architecture incorporating the Convolutional Block Attention Module (CBAM), enabling direct processing of multi-depth, multi-frame stacked images and adaptive enhancement of salient motion features in both spatial and channel dimensions. The method eliminates traditional manual screening, achieving fully automated detection and classification from raw image sequences. Results: Evaluated on approximately 2,000 real survey images, the model achieves 98.9% accuracy and an AUC of 0.992. With optimized detection thresholds, it maintains high recall while reducing human verification effort by over 99%. This work significantly advances the automation level and operational efficiency of moving object discovery in astronomical surveys.
📝 Abstract
One of the greatest challenges for detecting moving objects in the solar system from wide-field survey data is determining whether a signal indicates a true object or is due to some other source, like noise. Object verification has relied heavily on human eyes, which usually results in significant labor costs. In order to address this limitation and reduce the reliance on manual intervention, we propose a multi-input convolutional neural network integrated with a convolutional block attention module. This method is specifically tailored to enhance the moving object detection system that we have developed and used previously. The current method introduces two innovations. This first one is a multi-input architecture that processes multiple stacked images simultaneously. The second is the incorporation of the convolutional block attention module which enables the model to focus on essential features in both spatial and channel dimensions. These advancements facilitate efficient learning from multiple inputs, leading to more robust detection of moving objects. The performance of the model is evaluated on a dataset consisting of approximately 2,000 observational images. We achieved an accuracy of nearly 99% with AUC (an Area Under the Curve) of >0.99. These metrics indicate that the proposed model achieves excellent classification performance. By adjusting the threshold for object detection, the new model reduces the human workload by more than 99% compared to manual verification.