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The single-stage object detection technology represented by YOLOv8 has significant optimizations in the backbone network, but fails to efficiently integrate contextual information in the neck network, leading to missed and false detections in small object detection. Additionally, the large number of algorithm parameters and high computational complexity make it unsuitable for end-to-end industrial deployment. To address these issues, this paper introduce the BiFormer attention mechanism based on the Transformer structure to enhance the detection performance for small objects and improve the algorithm's accuracy. At the same time introduce the GSConv module to reduce the algorithm size while ensuring no adverse impact on its performance, balancing the increase in computational and parametric costs brought by BiFormer. An object detection algorithm named Bi-YOLO is designed to achieve a balance between lightweight and algorithm performance. Experimental results show that compared to YOLOv8n, the Bi-YOLO object detection algorithm improves algorithm accuracy by 4.6%, increases the small object detection accuracy on the DOTA dataset by 2.3%, and reduces the number of parameters by 12.5%. Bi-YOLO effectively achieves a balance between algorithm lightweight and performance, providing a new approach for end-to-end industrial deployment. [ABSTRACT FROM AUTHOR]