Precise-oil leakage segmentation for substation equipment under water-accumulation interference

doi:10.11996/JG.j.2095-302X.2026020296

Abstract

Abstract:

Accurate segmentation of oil leakage from substation equipment is crucial for ensuring the safe operation of power systems. However, existing segmentation methods face significant challenges due to the high visual similarity between oil leakage and water accumulation, the irregular morphology of oil spills, and the scarcity of training data for such interference scenarios. To address these issues, a comprehensive solution from data augmentation to network-model optimization was proposed. First, a novel diffusion model-based timestep-adaptive tuning method (Evolutionary Tuning, EvoTune) was designed to dynamically adjust the feature contributions of U-Net during image generation, effectively expanding the quantity and diversity of water interference scenarios in the dataset. Second, based on the data augmentation, a high-performance oil leakage segmentation network HyDR-Net (Hydro Discriminative Refining Network) was proposed. The network incorporated a Discriminative Boundary Anti-Interference Module (DBAIM) to enhance feature discrimination between oil leakage and confusing backgrounds such as water accumulation while effectively suppressing background noise, and a Multi-Scale Attentive Alignment Module (MAAM) for multi-scale context-aware processing and fine-grained boundary calibration of oil leakage features to accommodate the irregular morphology of oil spills. Experimental results showed that EvoTune achieved SSIM, PSNR, and NIQE scores of 0.918 8, 26.790 2 dB, and 5.713 3, respectively, significantly improving training-data quality and the realism of generated water-accumulation regions, while HyDR-Net achieved F1 and PA scores of 80.46% and 92.15%, respectively, substantially outperforming existing mainstream segmentation methods across all key evaluation metrics, and particularly exhibiting superior segmentation accuracy and robustness under complex water-interference scenarios. The research provided an effective approach for addressing data scarcity under specific visual-interference conditions and offered robust technical support for intelligent and precise detection of oil leakage in substation equipment.

Key words: oil-leakage segmentation, substation equipment, data augmentation, diffusion models, water-accumulation interference

CLC Number:

ZHAO Zhenbing, ZHANG Jingliang, TANG Chenkang, BI Yuxuan, LI Haopeng. Precise-oil leakage segmentation for substation equipment under water-accumulation interference[J]. Journal of Graphics, 2026, 47(2): 296-310.

Figures/Tables 12

Fig. 1 Overall network architecture diagram of timestep adaptive tuning method

Fig. 2 HyDR-Net network architecture diagram

Fig. 3 Network architecture diagram of discriminative boundary anti-interference model

Fig. 4 Network architecture diagram of difference-aware boundary refinement unit (DABRU)

Fig. 5 Network architecture diagram of multi-scale attentive alignment model

Table 1 The comparison results of image generation quality

方法	SSIM	PSNR/dB	NIQE
Dall-E 2	0.712 4	22.456 0	7.623 4
Imagen	0.738 9	23.127 0	7.289 1
SD	0.730 8	23.017 0	7.010 0
SD +LoRA	0.746 5	23.023 7	6.985 7
SD + LoRA +EvoTune	0.918 8	26.790 2	5.713 3

Fig. 6 Generation results of different methods ((a) Original image; (b) Dall-E 2; (c) Imagen; (d) SD; (e) SD+LoRA;(f) SD+LoRA+EvoTune)

Table 2 The comparison experimental results on substation equipment oil leakage segmentation dataset

方法	F1/%	IOU/%	PA/%	Params/M	FLOPs/G
ANN	73.61	58.24	88.71	43.93	190.0
DeeeplabV3+	75.42	60.54	90.64	41.29	182.0
UPerNet(Twins)	77.62	63.42	90.99	90.19	275.0
KNet + PSPNet	71.85	56.06	89.13	59.89	190.0
UPerNet(Swin)	73.80	58.47	90.58	122.88	306.0
DMNet	74.53	59.40	90.22	50.88	201.0
TransUnet	75.78	61.01	90.41	91.52	128.0
SegFormer	76.48	61.91	90.79	27.36	56.9
DSACP	77.95	63.87	91.38	42.48	101.0
SAM	68.99	41.28	85.26	89.70	677.0
Ours	80.46	67.31	92.15	32.83	239.0

Fig. 7 Visualization results of Nemenyi test

Fig. 8 Segmentation results of oil leakage by different networks under water accumulation interference scenarios ((a) Original image; (b) Ground truth; (c) ANN; (d) SegFormer; (e) UPerNet (Twins); (f) DSACP; (g) HyDR-Net)

Table 3 The ablation experimental results of HyDR-Net

基线	DBAIM	MAAM	F1/%	IOU/%	PA/%
√			76.48	61.91	90.79
√	√		78.88	65.13	91.64
√		√	78.84	65.07	91.53
√	√	√	80.46	67.31	92.15

Fig. 9 Heatmap visualization results of deep feature xfuse from different networks ((a) Original image; (b) Ground truth; (c) Baseline network; (d) Baseline+DBAIM; (e) Baseline+MAAM; (f) Baseline+DBAIM+MAAM)

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