Against the complex detection challenges posed by the multi-scale, multi-category, and dense distribution of objects in the daily item recognition task for intelligent household service robots, an improved YOLOv10-DLC object detection algorithm is proposed: By designing a diverse branch module for feature fusion (C2f-DBB), the algorithm network′s ability to detect objects of different scales is enhanced; By adopting a combined module of fast spatial pyramid pooling and large kernel separable convolution attention (SPPF-LSKA), the algorithm network′s ability to understand the spatial structure of features is improved, and the key features of objects are highlighted; By using the content-aware feature reassembly (CARAFE) operator, the accuracy of detail reconstruction during image upsampling in the network is improved. Experimental analysis shows that the improved YOLOv10-DLC algorithm has improved the accuracy of object detection, with its mAP value reaching 94.3%, an increase of 3.6% compared with the original algorithm network model.