Background: Fire incidents occur in complex scenarios, where the dynamic positions and varying scales of flames and smoke pose challenges for fire detection. To improve the stability, localization accuracy, and detection precision of small targets in fire detection, a fused PBCA method for fire and smoke object detection has been proposed in this paper, called FS-YOLOv8. Background: Fire incidents occur in complex scenarios, where the dynamic positions and varying scales of flames and smoke pose challenges for fire detection. To improve the stability, localization accuracy, and detection precision of small targets in fire detection, a fused PBCA method for fire and smoke object detection has been proposed in this paper, called FS-YOLOv8. Objective: The objective of this approach was to improve the detection accuracy of flames and smoke, enhance the robustness of the system, and strengthen the feature representation capability. It aimed to optimize the contribution of feature maps at different scales, allowing the network to capture inter-channel correlations while preserving precise localization information of the targets. Furthermore, it aimed to enhance the learning ability of small-scale flame and smoke objects. Method: Firstly, DCN (Deformable Convolutional Network) was integrated into the CSPDarknet backbone network to extract features from flame and smoke images. Subsequently, a module called PBCA was designed by combining BiFPN (Bidirectional Feature Pyramid Network) and coordinate attention. Finally, a small object detection layer, YOLO HEAD-4, was constructed. Results: The experimental results of our proposed FS-YOLOv8 method on a self-made dataset demonstrated improved detection accuracy compared to other conventional methods. Therefore, the FS-YOLOv8 method effectively enhanced the performance of object detection in fire and smoke scenarios. Conclusion: The FS-YOLOv8 method has been found to effectively improve the performance of object detection in fire and smoke scenarios, enhance the robustness of the system, strengthen the feature representation capability, and amplify the learning ability of small-scale flame and smoke objects. other: NO