Fabric seams detection in industrial setting is becoming increasingly important in textile applications. However, seam detection faces challenges such as small target size, few available features, and complex environmental factors, which make it difficult to ensure stable and real-time detection results. A fabric seam detection algorithm YOLOv8-DVB based on improved YOLOv8 is proposed to address this series of problems. The C2f module is optimized based on the characteristics of Deformable Convolutional Networks v4, a C2f-DCN module with multi-size feature sampling is proposed to strengthen the network"s extraction of feature information of different sizes. In the neck, the BiFPN structure is used as a feature fusion approach, which allows features of different scales to be more fully fused at multiple levels by introducing top-down and bottom-up bidirectional pathways. Additionally, a more efficient VoV-GSCSP module is introduced to lightweight the feature fusion network, which helps the neck network to reduce the computational load and parameter count. Finally, a dedicated small target detection layer is designed to optimize the feature extraction of small targets. YOLOv8-DVB is compared with the original model as well as YOLOv5, YOLOv7, and Faster R-CNN through experiments to verify the detection accuracy and detection precision. The experimental results show that the method obtains 84.7% detection accuracy on the self-constructed dataset, which is higher than the original model and other network models, and is able to quickly and effectively accurately detect the target categories and locations in complex industrial environments.