Low illumination environments can lead to situations such as inconspicuous image target features and severe noise interference, which affect the detection performance of the object detector.To address the above problems, a multi-scale image feature enhancement module FEM is constructed, and in conjunction with YOLOv8s object detection network, an end-to-end low-light image object detection method FE-YOLO is constructed.Firstly, FEM is employed to extract feature information from the input image at three different scales and efficiently fuse them to obtain an enhanced image with rich feature representation.Then, in the neck network of YOLOv8s, a target feature enhancement module TFE is incorporated. TFE works by suppressing background noise information in higher-level features, thereby accentuating the representation capacity of target features.The experimental results show that the mean average precision mean (mAP) on the low-light image object detection dataset ExDark reaches 75.63%, which is 3.03% higher than the original YOLOv8s algorithm, and this paper′s algorithm achieves a better detection result in the low-light object detection task.