The accurate identification of the hip joint keypoint is vital for diagnosing developmental dysplasia of the hip. However, in pediatric hip X-ray images, bone regions around key points often exhibit low contrast and blurred edges, resulting in unclear edge features. Furthermore, down-sampling operations during feature extraction further weaken edge information. Key structures surrounding the keypoint are highly susceptible to background interference. Such factors hinder the precise localization of key points. An edge feature and detail-aware integrated YOLOv8s algorithm was proposed for hip joint key point detection. The algorithm designs an edge feature enhancement module to capture spatial information around key points and strengthen edge features. A detail-aware network was designed to integrate and refine multi-level features, enhancing image perception of fine structures. Experiments used a hip X-ray dataset from the Department of Radiology, Children's Hospital of Chongqing Medical University. Results showed reductions in average keypoint localization and angular errors to 4.2090 pixel and 1.4872°, respectively. These reductions, which are 6.8% and 9.9% compared to those of YOLOv8s, highlight significant improvements in detection accuracy. The algorithm enhances keypoint detection precision and provides valuable support for clinical diagnosis.