An instance segmentation network for discharging carbon traces inside oil-immersed transformers with boundary and detail features enhancement.

Constrained by the closed structure, it is difficult to visually detect the insulation defects inside large oil-immersed transformers. For this issue, this paper uses a self-developed micro-robot for visual inspection. Since the defects to be detected have different shapes, large differences in size, and complex background, accurately and quickly detecting the defect target is the key for the micro-robot to complete the internal inspection. To this end, a novel network consisting of C2f-DySnake module and Efficient Pyramid module for Carbon Trace Segmentation (CDEP-CTSeg) is proposed in this paper. To address the overexposure and color distortion of carbon traces resulting from variable imaging distances and fluctuating lighting conditions, an improved Retinex image enhancement algorithm is proposed for image preprocessing. It can enhance local contrast and detail at multiple scales while improving overall contrast and brightness. Furthermore, to tackle the inaccurate boundary segmentation of complex carbon traces, such as branch-like forms or other elongated continuous forms, the C2f-DySnake module is integrated into the backbone of the YOLOv8 framework, which is capable of capturing features of branched structures by adaptively focusing on elongated and curved local details. Additionally, to address the missed detection of small carbon traces resulting from significant size variations, an EHSPAN feature pyramid network is designed and integrated into the Neck network. It improves the model's ability to capture relevant characteristics and reduces missed detections for smaller targets by merging selected information with high-level features. Experimental results showed that compared with the traditional YOLOv8-seg model, the recall rate, precision, and mAP50 of the proposed CDEP-CTSeg network were improved by 2.3%, 2.8%, and 2.8% respectively. Furthermore, the proposed network improved the Average Precision of clustered and dendritic carbon traces by 1.9% and 3.7% respectively. It indicates that the proposed CDEP-CTSeg network achieves fast and accurate segmentation of insulation defects inside the transformer, which contributes to the stable operation of large oil-immersed transformers.