Blood cell detection is a critical tool for diagnosing various diseases, as changes in blood cell count and morphology often reflect a person′s health condition. However, manual detection is time-consuming and prone to errors and omissions. To address these challenges, this paper presents an improved blood cell detection algorithm based on the YOLOv7 framework, named YOLOv7-SMC. The algorithm integrates spatial and channel reconstruction convolution to reduce feature redundancy and enhance performance. Additionally, a mixed local channel attention is incorporated in the neck network to strengthen the model′s representational capability. The algorithm also replaces the nearest neighbor interpolation upsampling with a content-aware reassembly of features upsampling operator, which adaptively adjusts the upsampling strategy to produce detailed and smooth results. Furthermore, a minimum point distance intersection over union loss function is introduced to simplify the similarity comparison between bounding boxes. Experimental results on the BCCD dataset demonstrate that this algorithm improves the mean average precision at IoU thresholds of 0.5 and 0.5:0.95 by 2.6% and 2.9%, respectively, indicating its high practicality and accuracy.