A lightweight YOLOv4s based on an attention mechanism is proposed to address the low accuracy and slow speed issue of the general target detection algorithm in multi-target life scenes. First, CSPDarknet53-s was used as the backbone network to extract image features, and these features were selected using the attention block. Subsequently, the feature pyramid network was adopted to fuse the features. Finally, the YOLOv4s head was used to process the two outputs after the feature fusion to improve the multi-target detection ability in living scenes. According to the experiment results, the YOLOv4s algorithm outperforms the prior algorithm in the PASCAL VOC and MS COCO datasets, exhibiting improvement in the mean average precision and average precision. Compared with the lightweight algorithm Efficientdet, the YOLOv4s algorithm also has a certain improvement in the AP on the MS COCO dataset, and achieves effective significant target detection.