GFRF R-CNN:Object Detection Algorithm for Transmission Lines

To maintain the reliability of power systems,routine inspections using drones equipped with advanced object detection algorithms are essential for preempting power-related issues.The increasing resolution of drone-captured images has posed a challenge for traditional target detection methods,especially in identifying small objects in high-resolution images.This study presents an enhanced object detection algorithm based on the Faster Regionbased Convolutional Neural Network(Faster R-CNN)framework,specifically tailored for detecting small-scale electrical components like insulators,shock hammers,and screws in transmission line.The algorithm features an improved backbone network for Faster R-CNN,which significantly boosts the feature extraction network’s ability to detect fine details.The Region Proposal Network is optimized using a method of guided feature refinement(GFR),which achieves a balance between accuracy and speed.The incorporation of Generalized Intersection over Union(GIOU)and Region of Interest(ROI)Align further refines themodel’s accuracy.Experimental results demonstrate a notable improvement in mean Average Precision,reaching 89.3%,an 11.1%increase compared to the standard Faster R-CNN.This highlights the effectiveness of the proposed algorithm in identifying electrical components in high-resolution aerial images.

Influence of hydraulic retention time on behavior of antibiotics and antibiotic resistance genes in aerobic granular reactor treating biogas slurry

The behavior of antibiotics and the corresponding resistance genes in aerobic granular reactors for treating biogas slurry under different hydraulic retention times (10.7 h, Rl;8 h, R2) was investigated in this study. The results indicated that the hydraulic retention time could affect the effluent concentrations and removal efficiencies of sulfonamides. The average removal rates of tetracyclines, fluoroquinolones, and sulfonamides were 63%, 46%, and 90% in Rl, and 62%, 46%, and 86% in R2, respectively. Although the removal efficiencies of tetracyclines and fluoroquinolones were similar in both reactors, the respective accumulated concentrations of tetracyclines and fluoroquinolones in R1 were 7.00 and 11.15μg/g SS, which were lower than those in R2 (8.92 and 13.37μg/g SS, respectively). The difference in the relative abundance of target antibiotic resistance genes between both reactors was not significant, yet the average relative abundances of all target resistance genes in R1 were higher than those in R2 after 45 days of operation. The results of this study suggested that a longer hydraulic retention time could enhance the antibiotic removal ability of aerobic granular sludge, yet it may also increase the risk of surplus sludge utilization from a resistance genes point of view.