FSS-YOLO: The Lightweight Drill Pipe Detection Method Based on YOLOv8n-obb

To achieve fast and accurate identification of drill pipes, we propose FSS-YOLO, which is a lightweight drill pipe detection method based on YOLOv8n-obb. This method first introduces the FasterBlock module into the C2f module of YOLOv8n-obb to reduce the number of model parameters and decrease the computational cost of the model and redundant feature maps. Next, the SimAM attention mechanism is added to the backbone network to enhance the weight of important features in the feature map and improves the model's feature extraction capability. In addition, using shared convolution to optimize the detection head, which not only lightens the detection head but also enhances its ability to learn features of different scales, improving the model's generalization ability. Finally, the FSS-YOLO algorithm is validated on the self-built dataset. The results show that compared with the original algorithm, FSS-YOLO achieves improvements of 5.1% in mAP50 and 11.5% in Recall, reduces the number of parameters by 45.8%, and achieves an inference speed of 27.8ms/frame on Jetson Orin NX. Additionally, the visual detection results for different scenarios demonstrate that the improved YOLOv8n-obb algorithm has promising application prospects