The three-dimensional perception of road objects in challenging environments is crucial for the development of autonomous vehicles capable of operating in all conditions, at all hours. Infrared binocular vision mimics the human binocular system, facilitating stereoscopic perception of objects in challenging conditions such as dim or zero-light environments. The core technology for stereoscopic perception in binocular vision systems lies in accurate object detection and matching. In response to the inefficiency of the sequential implementation of object detection and matching, a synchronous object detection and matching network (SODMNet) is proposed, which is capable of synchronous detection and matching of infrared objects. SODMNet innovatively combines a object detection network with a object matching module, leveraging the deep features from the classification and regression branches as inputs for the object matching module. By concatenating these features with relative position encodings from the feature maps and processing them through a convolutional network, the network generates feature descriptors for the left and right images. The object matching is then achieved by calculating the Euclidean distances between these descriptors, thus facilitating synchronous object detection and matching in binocular vision. In addition, a novel nighttime infrared binocular dataset, annotated with targets such as pedestrians and vehicles, is presented to support the development and evaluation of the proposed network. Experimental results indicate that SODMNet achieves a significant improvement of over 84.9% in object detection mean average precision (mAP) on this dataset, with a object matching average precision (AP) of 0.5777. These results demonstrate that SODMNet is capable of high-precision, synchronized object detection and matching in infrared binocular vision, marking a significant advancement in the field.