Bibliographic Details
| Title: |
煤矿井下煤矸低质图像增强识别算法. |
| Alternate Title: |
Image enhancement and recognition algorithms for low-quality coal gangue in underground coal mines. |
| Authors: |
李德永1,2,3,4 ldyziyou@126.com, 高员博1,2, 郭永存1,2, 王 爽1,2, 杨宇豪1,2, 张思航1,2 |
| Source: |
Coal Science & Technology (0253-2336). May2026, Vol. 54 Issue 5, p94-108. 15p. |
| Subject Terms: |
*Image enhancement (Imaging systems), *Object recognition (Computer vision), *Deep learning, *Coal mining, *Signal denoising, *Image processing |
| Abstract (English): |
To address the issue of insufficient illumination in coal mine shafts leading to low brightness and contrast in captured images, which in turn affects the feature extraction performance of coal gangue detection algorithms, this paper proposes an object detection method suitable for low-light environments underground. This method incorporates a low-light image enhancement module and an object detection module. First, the low-light image enhancement module improves the visual quality of raw, low-fidelity images and restores essential structural, textural, and chromatic features. Subsequently, the enhanced images are fed into the object detection module for accurate coal gangue recognition. Within the enhancement module, we introduce the MF-CG-LIME algorithm, an advancement over MF-LIME. This algorithm uses dark channel guidance and adaptive threshold segmentation to optimize brightness adjustment and weight calculation, thereby amplifying the contrast between coal gangue and background while preserving critical textures. To combat residual noise, we design the Enhanced Residual Attention Denoising Network (ERADNet), which leverages a reinforced residual architecture. By integrating cascaded residual blocks, skip connections, and channel-spatial attention mechanisms, ERADNet effectively targets noisy regions and salient feature channels, maintaining a precise balance between noise reduction and detail preservation. This substantially enhances key discriminative features for coal gangue detection. For object detection, we propose the ELW-YOLO module, specifically tailored for underground coal gangue recognition. Building upon the YOLOv10s framework, this module incorporates EfficientNetV1 components into the backbone to optimize the depth-width-resolution ratio, improving the extraction of subtle gangue features. The addition of the LSKA attention mechanism in the neck network further boosts adaptability and accuracy for multi-scale feature extraction. The WIoU loss function dynamically optimizes center point alignment and balances sample learning weights, improving the precision and stability of coal gangue boundary localization. Experimental results show that our image enhancement algorithm outperforms existing methods across all evaluation metrics. ELW-YOLO achieves an average precision of 90.8%, a 3.8% gain over original YOLOv10s, and delivers the best overall performance on images enhanced by our approach, reaching an average frame rate of 73.7 frames per second. This enables real-time detection and provides technical support for target detection in the low-illumination, complex environments of underground coal mines. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对煤矿井下光照不足导致拍摄图像亮度和对比度偏低, 进而影响煤矸检测算法特征提取效 果的问题, 提出一种适用于井下低照度环境的目标检测方法。该方法包含低光图像增强模块和目标 检测模块, 先通过低光图像增强模块改善原始低质量图像的视觉效果, 还原各类图像特征, 再利用 目标检测模块对增强后的图像进行目标识别。在低光图像增强模块中, 基于 MF-LIME 算法提出煤矸 亮度增强算法 MF-CG-LIME, 通过暗通道引导与自适应阈值分割优化亮度调整与权重计算, 从而有 效保留煤矸纹理、扩大煤矸与背景的亮度差; 针对增强后仍存在的噪声干扰问题, 进一步设计基于 增强残差结构的去噪网络 ERADNet, 通过级联残差块与跳跃连接缓解梯度消失, 嵌入通道与空间注 意力机制聚焦噪声区域与关键特征通道, 实现噪声抑制与细节保留的精准平衡, 从而强化煤矸关键 判别特征。在目标检测模块中, 提出煤矿井下煤矸检测模块 ELW-YOLO。该检测模块以 YOLOv10s 模型为框架, 在主干网络中引入 EfficientNetV1 模块, 优化网络的深度、宽度和分辨率之间的比例, 提高对煤矸弱特征的提取能力; 在颈部网络中添加 LSKA 注意力机制, 强化对不同尺度煤矸特征提 取的适应性和准确度; 采用 WIoU 损失函数动态优化中心点对齐和平衡样本学习权重, 提升煤矸边 界定位的精准度与稳定性。试验结果表明: 所提图像增强算法各项指标均优于其他对比算法, ELWYOLO 的平均精度均值达到 90.8%, 相较于原 YOLOv10s 提高了 3.8% ;与其他算法进行对比, 在本 文增强算法图像上使用 ELW-YOLO 进行检测的综合性能最佳, 且平均帧率达 73.7 帧/ s, 可实现实时 检测, 能够为煤矿井下低照度复杂环境下的目标检测提供技术支撑。 [ABSTRACT FROM AUTHOR] |
| Database: |
Energy & Power Source |
