SNIPER Based Multi-Target and Multi-Scale Aerial Image Processing Method
摘要：The maintenance of power transmission system appeals automatic inspection system to replace manual inspection. Inspection based on Unmanned Aerial Vehicle（UAV） generates a huge number of photos to be processed. These aerial photos have several challenging features:high density,small,scale variation. In order to ameliorate the performance on detection of important objects as insulators and pin bolts,an efficient multiscale training algorithm called SNIPER is introduced. SNIPER enhanced Faster RCNN was trained on a powerline dataset for object detection. SNIPER provides a good average precision and average recall on large objects like insulators and medium objects with adequate annotated instances like pin bolts and dampers. However,SNIPER fails to locate missing pin or displaced pin,possibly due to their similarity to pin bolts. Future development of a SNIPER-based cascaded detection scheme could help detect defected small objects.
The 2020 International Conference on Ubiquitous Power Internet of Things （UPIOT 2020） and 4th International Symposium on Green Energy and Smart Grid （SGESG 2020）