改进YOLOv7算法及其油田生产违规行为检测OA

To address the challenges in oilfield site monitoring caused by high camera installation angles and small target sizes,we propose an improved YOLOv7 object detection algorithm.Firstly,a GD fusion mechanism is introduced into the neck network to integrate feature maps from different levels,enhancing detection capability for multi-scale targets,especially small ones.Secondly,the Biformer module is added,and its dual-branch routing attention mechanism is utilized to analyze the correlation between features from a global perspective,effectively filtering out background interference and redundant features,enhancing the model's attention to key target areas.At the same time,the attention sparsity strategy is combined to reduce redundant computations,balancing detection accuracy and computational efficiency.Finally,the standard IoU loss is replaced with ICoU-NWD loss,which uses Wasserstein distance for more accurate bounding box localization,especially for targets with large scale variations.Experimental results show that the mAP of the improved model in typical oilfield scenarios reaches 97.0%,which is 7.2%higher than that of the original YOLOv7.While enhancing the detection accuracy and feature expression capabilities,the number of parameters only increases by 12.1%,and the computational load only rises by 3.7%.It is suitable for deployment on edge computing devices to meet the intelligent detection requirements in complex environments.