Based on the convolutional neural network (CNN), a novel technique is investigated for lost gamma source detection in a room. The CNN is trained with the result of a GEANT4 simulation containing a gamma source inside a meshed room. The dataset for the training process is the deposited energy in the meshes of different n-step paths. The neural network is optimized with parameters such as the number of input data and path length. Based on the proposed method, the place of the gamma source can be recognized with reasonable accuracy without human intervention. The results show that only by 5 measurements of the energy deposited in a 5-step path, (5 sequential points 50 cm apart within 1600 meshes), the gamma source location can be estimated with 94% accuracy. Also, the method is tested for the room geometry containing the interior walls. The results show 90% accuracy with the energy deposition measurement in the meshes of a 5-step path.

基于卷积神经网络（CNN），研究了一种用于房间内丢失伽马源检测的新技术。CNN 使用包含网格房间内伽马源的 GEANT4 模拟的结果进行训练。训练过程的数据集是不同 n 步路径的网格中的沉积能量。神经网络使用输入数据的数量和路径长度等参数进行优化。基于所提出的方法，可以在没有人为干预的情况下以合理的精度识别伽马源的位置。结果表明，仅通过 5 步路径中沉积的能量的 5 次测量（1600 个网格内相距 50 cm 的 5 个连续点），伽马源位置的估计准确度为 94%。此外，该方法还针对包含内墙的房间几何形状进行了测试。