In the field of object detection, it is challenging to achieve a balance between neck complexity and accuracy. To address this issue, we propose an efficient and decoupled neck module called shared feature pyramid network (Shared-FPN). Not only does Shared-FPN not increase the number of model parameters and floating point operations per second (FLOPs), but it also can be easily ported to any of the detection models. It improves on path aggregation feature pyramid network (PAFPN) by using transposed convolution with a large convolution kernel as the upsampling module, designing spatial pyramidal pooling-fast downsampling (SPPFD) based on shared pooling, and designing shared convolution as the right part module. To evaluate the performance of Shared-FPN in object detection tasks, we conducted experiments on object detection datasets. The results show that Shared-FPN achieved excellent performance across all sizes. In particular, on the VOC 2012 dataset, the Shared-FPN’s mean average precision (mAP) was improved by 11.2% compared to FPN with you only look once extended-s (YOLOX-s) as the detector. On the COCO dataset, the Shared-FPN’s mAP was improved by 7.8% compared to FPN and 7.1% compared to PAFPN with faster region-based convolutional neural network (Faster RCNN) as the detector. The Shared-FPN can be easily inserted into any of the detectors for better performance in various scenarios, such as small, medium or large objects.