Uncertainty-aware Oriented Object Detection for Trustworthy Quality Inspection of Secondary Wiring
Secondary wiring quality inspection is designed to check whether the number on the terminal block matches the number of the wired cable cap.Due to the characteristics of small size,dense distribution and different orientations of wiring caps,horizontal object detection algorithm performs poorly on this task,while the noise interference during image acquisition exacerbate the misdetection of difficult case samples.To this end,an uncertainty-aware Real-time Oriented Object Detection(UROD)algorithm is proposed and applied to the trusted quality inspection of substation secondary wiring.Specifically,based on the YOLOv8 algorithm that introduces an angular regression branch to realize the rotating object detection function,and models its regression and classification branches with Gaussian distributions.UROD can output the object detection results and its uncertainty metric.And the uncertainty metric can also be used in the wiring pairwise strategy.The experimental results on dataset DOTAv1 and the secondary wiring dataset show that compared to the baseline method YOLOv8,the UROD algorithm improves the accuracy of the secondary wiring quality inspection.Whereas compared to mainstream rotated object detection algorithms,UROD algorithm not only improves the detection speed,but also can reject difficult case samples based on the uncertainty.
oriented object detectionuncertainty-awareGaussian distribution modelingsecondary wiring quality inspectionYOLOv8
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