In order to solve the problems of single battery style, less feature information and limited academic scenario, we focus on lightweight detection algorithm, and propose an electric bicycle battery detection algorithm based on an improved version of YOLOv8, termed PSPG-YOLO. In terms of feature extraction within the network mesh, have designed a multi-branch PStarblock to optimize C2f module enhancing the featureinformation representational capacity with a simpler module. For multi-scale fusion feature pyramid, dilated convolution with shared parameters is employed to refine the SPPF structure, increasing receptive fields while preserving more detailed feature information. regarding detection head design, we introduce a GSPH. By leveraging shared convolution alongside partial convolution techniques, we reduce model complexity，additionally, both anchor point and stride can be dynamically adjusted; internal parameters are automatically calibrated to enhance adaptability across various scale feature maps. Experiments conducted on a self-constructed data-set specifically targeting electric bicycle batteries demonstrate that, compared to the baseline model YOLOv8n, PSPG-YOLO achieves reductions in FLOPs by 57% and in params by 43%,and improved mAP50 values by 0.8，yielding superior overall performance relative to other mainstream detection models，providing an efficient management method for electric bicycle battery illegal entry into households.