In order to address the challenge of inefficient and difficult visual identification and localization of car keys in complex home environments,this paper designs a real-time detection system based on the E-YOLOv8n model. This system captures video streams through a wireless USB camera, performs real-time object detection using a computing terminal equipped with the E-YOLOv8n model, and feeds back the detection results via an audio alarm module. The E-YOLOv8n model is the core component of the system, incorporating several key improvements: first, the network structure is optimized by reconstructing the backbone network with DSConv and streamlining the P5 output to reduce computational redundancy. Second, a DSPPF module is designed to enhance multi-scale feature fusion while reducing computational cost. Third, a Coord attention mechanism module is embedded at the end of the backbone network to focus on key features through coordinate attention and suppress background interference. Finally, a lightweight detection head, the LWD module, is adopted to maintain detection accuracy while improving computational efficiency. Based on a self-constructed car key dataset, experimental results demonstrate that compared to the original YOLOv8n model, the E-YOLOv8n model reduces computational load, parameter count, and model size by 53.8%, 32.8% and 52.4%, respectively, while improving precision by 1.7%. These enhancements achieve significant lightweighting while boosting performance, making it more suitable for deployment on resource-constrained devices commonly found in home environments.