Plenty of studies on the safe and efficient transportation of coal in mines reveal that the belts often suffer some hazards caused by foreign objects such as large gangue, bolts and other foreign bodies scratching, tearing the belt, and blocking the coal discharge point in the process of coal transportation．To overcome the problems of large amount of network parameters, poor real-time performance, and low recognition accuracy in the current classification and recognition of belt foreign objects, a lightweight network that integrates residual information is proposed, and we release the coal mine underground foreign object dataset CUMT-BelT.

皮带在煤炭输送过程中存在大块矸石、锚杆等异物划伤、撕裂皮带和堵塞落煤口等安全隐患，预警、分选及联动控制不及时会严重影响煤炭的运输效率。为克服当前对皮带异物分类识别时存在的网络参数量大、实时性差、识别精度低等问题，提出了一种融合残差信息的轻量级网络，并开源了煤矿井下异物数据集CUMT-BelT。

Paper：融合残差信息轻量级网络的运煤皮带异物分类.
CUMT-BelT Dataset：(pwd:z39g).

The images in coal mines have problems of dim, blurry and unclear edges. To address these issues, this article proposes a lightweight mine image super-resolution reconstruction method that fuses hierarchical features and attention mechanism. In addition, to make the proposed model have better generalization performance in real-mine scenes, a coal mine underground image dataset CMUID is constructed for the training and testing experiments of the network model.

针对矿井图像灰暗模糊、边缘不清晰等问题，提出了一种融合层次特征和注意力机制的轻量化矿井图像超分辨率重建方法。为了使模型在真实矿井场景中具有更好的泛化能力，构建了一种煤矿井下图像数据集CUMT-CMUID用于网络模型的训练和测试实验。

Paper1：融合层次特征和注意力机制的轻量化矿井图像超分辨率重建方法.
Paper2：Structure-Preserving and Color-Restoring Up-Sampling for Single Low-Light Image
Paper3：Light-Guided and Cross-Fusion U-Net for Anti-Illumination Image Super-Resolution
CUMT-CMUID Dataset：(pwd:249a).

In recent years, national policies on intelligent construction of coal mines have been introduced one after another, and the coal mining industry has been paying more and more attention to the construction of intelligent mines with "intelligence and safety" as the core, wearing helmets is the most basic and important measure to protect personnel safety in coal mining. For this reason, our group collected helmet wearing data from underground personnel and established an underground helmet detection data set to help the construction of intelligent mines.

近年来，国家关于煤矿智能化建设的政策相继出台，煤矿行业越来越重视以“智能、安全”为核心的智慧矿山建设，佩戴安全帽是煤矿生产中保护人员安全的最基本也是最重要的措施。为此课题组专门收集井下人员安全帽佩戴数据，建立了井下安全帽检测数据集CUMT-HelmeT助力智慧矿山的建设。

CUMT-HelmeT Dataset：(pwd:d2x2).

[1] 程德强等.融合层次特征和注意力机制的轻量化矿井图像 超分辨率重建方法[J].仪器仪表学报,2022,43(8):73-84.
[2] 程德强等.融合残差信息轻量级网络的运煤皮带异物分类[J].煤炭学报,2022,47(3):1361-1369.
[3] liangliang Chen, et al. Structure-Preserving and Color-Restoring Up-Sampling for Single Low-Light Image[J]. IEEE Transactions on Circuits and Systems for Video Technology,2022,32(4): 1889-1902. [4] Cheng Deqiang, et al. Light-Guided and Cross-Fusion U-Net for Anti-Illumination Image Super-Resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology,2022, 32(12): 8436-8449.

[1] CHENG Deqiang, et al. Lightweight network based on residual information for foreign body classification on coal conveyor belt[J]. Journal of China Coal Society, 2022, 47(3): 1361-1369．
[2] CHENG D Q, et al. Lightweight super-resolution reconstruction method based on hierarchical features fusion and attention mechanism for mine image [J]. Chinese Journal of Scientific Instrument, 2023, 43(8): 73-84.
[3] liangliang Chen, et al. Structure-Preserving and Color-Restoring Up-Sampling for Single Low-Light Image[J]. IEEE Transactions on Circuits and Systems for Video Technology,2022,32(4): 1889-1902.
[4] Cheng Deqiang, et al. Light-Guided and Cross-Fusion U-Net for Anti-Illumination Image Super-Resolution[J]. IEEE Transactions on Circuits and Systems for Video Technology,2022, 32(12): 8436-8449.