The detection of intensity peaks, which correspond to atom positions, in high-resolution (scanning) transmission electron microscopy images is of great practical significance. By quantitatively determining the locations of these peaks, it is possible to obtain important information such as the structural deformation and the electric dipole distribution inside a material on the nanoscale. The detection of the peak positions in image processing can be regarded as a target detection problem, for which breakthroughs have been made with deep-learning neural networks. Comparing to the traditional target detection algorithms, which are based on specifically designed feature extractor and classifier, the deep-learning approach can obtain the features at multiple levels of abstraction automatically, thus improving the robustness of the detection process. In this paper, we realize the automatic detection of the intensity peaks in high-resolution electron microscopy images by building a high-quality atomic image sample set and using the YOLOv3 target detection framework. With its accuracy and speed, which are superior over other target detection neural networks, the YOLOv3 is suitable for image processing as the number of images increases explosively. The YOLOv3 network converges well in the training process using our atomic image sample set, with the loss function reaching a minimum after 500 epoches; the trained neural network can detect almost all the major atoms in the images, showing its excellent ability. With the aid of YOLOv3, we also develop a program to organize the detected atoms, enabling the detection of all the other atoms within each unit cell. It is found that, combining YOLOv3 with the newly developed algorithm, almost all the atoms can be successfully determined, showing its advantages over previous algorithms based on bravis lattice construction, especially for high-resolution transmission electron microscopy images with lattice defects. Our results show that this image processing technique has the potential in overcoming the bottleneck in the fast processing of high resolution electron microscopy images.