Work

MinePD-Net: Distilling Engineering Outputs and Intervention Masks for Underground Coal-Mine Video Enhancement

Year: 2026

Type: article

Abstract: We study lightweight approximation of a fixed engineering enhancement operator while limiting changes to usable image regions. Training only on corrected images leaves the pipeline’s intermediate intervention decisions unused. We propose MinePD-Net, which jointly distills corrected RGB outputs and native glare and dark-noise masks from a fixed, non-neural teacher. Predicted masks gate a bounded residual over an input identity path, limiting correction amplitude where intervention scores are low. A region-balanced objective assigns teacher reconstruction and raw-image identity to disjoint teacher-effect and clean-core regions, while retaining the trade-off between native-mask fidelity and transition reconstruction. This design couples decision transfer with correction control in a compact depthwise-separable encoder–decoder. Evaluation on UCM-WFE uses 4888 paired 2688 × 1520 frames from four fixed cameras at a single underground working face, source-clip-separated testing, repeated training, and leave-one-camera-out validation. Relative to a backbone-matched direct regressor, MinePD-Net improves reference peak signal-to-noise ratio by 0.33 dB and reduces clean-core identity error and flow-aligned temporal error by 36.1% and 14.8%, respectively. At native resolution, the 25,600-parameter model reaches 26.68 dB reference PSNR and 0.9234 SSIM. Network-only FP16 inference delivers 26.2 fps on an RTX 4070 Ti (batch size one), while the measured single-stream gateway-to-detection path delivers 17.90 fps with 28.40% dropped frames at a 25 fps input rate. A frozen equipment detector evaluated against human annotations improves from 0.446 to 0.472 mean average precision across intersection-over-union thresholds of 0.50–0.95, with a paired gain of +0.026 (95% CI: +0.009 to +0.043). Reference and mask scores quantify fidelity to the teacher’s outputs and decisions, not recovery of independently known clean scenes. Input preservation and human-annotated detection assess complementary benefits.

Source: Applied Sciences

DOI: 10.3390/app162010218