基于长短期存储的聚合增强型煤矸石视频识别模型

杨军

doi:10.13272/j.issn.1671-251x.18058

基于长短期存储的聚合增强型煤矸石视频识别模型

doi: 10.13272/j.issn.1671-251x.18058

杨军

国家能源集团宁夏煤业有限责任公司枣泉煤矿, 宁夏银川　750001

基金项目: 陕西省秦创原“科学家+工程师”队伍建设项目（2022KXJ-38）。

详细信息

作者简介:
杨军（1982—），男，宁夏平罗人，工程师，主要从事煤矿智能化技术研究工作，E-mail:273364857@qq.com

中图分类号: TD67
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- 被引次数: 0
出版历程
- 收稿日期: 2022-11-22
- 修回日期: 2023-02-20
- 网络出版日期: 2023-03-27

Aggregation enhanced coal-gangue video recognition model based on long and short-term storage

YANG Jun

Zaoquan Colliery, CHN Energy Ningxia Coal Industry Co., Ltd., Yinchuan 750001, China

摘要

摘要: 采用煤矸石图像识别技术进行煤矸石识别会错过一些关键目标的识别。视频目标识别模型比图像目标识别模型更贴近煤矸石识别分选场景需求，对视频数据中的煤矸石特征可以进行更广泛、更有深度的提取。但目前煤矸石视频目标识别技术未考虑视频帧重复性、帧间相似性、关键帧偶然性对模型性能的影响。针对上述问题，提出了一种基于长短期存储（LSS）的聚合增强型煤矸石视频识别模型。首先，采用关键帧与非关键帧对海量信息进行初筛。对煤矸石视频帧序列进行多帧聚合，通过时空关系网络 (TRN)将关键帧与相邻帧特征信息相聚合，建立长期视频帧和短期视频帧，在不丢失关键特征信息的同时减少模型计算量。然后，采用语义相似性权重、可学习权重和感兴趣区域（ROI）相似性权重融合的注意力机制，对长期视频帧、短期视频帧与关键帧之间的特征进行权重再分配。最后，设计用于存储增强的LSS模块，对长期视频帧与短期视频帧进行有效特征存储，并在关键帧识别时加以融合，增强关键帧特征的表征能力，以实现煤矸石识别。基于枣泉选煤厂自建煤矸石视频数据集对该模型进行实验验证，结果表明：相较于记忆增强全局−局部聚合（MEGA）网络、基于流引导的特征聚合视频目标检测（FGFA）、关系蒸馏网络（RDN）、视频识别的深度特征流（DFF）模型，基于LSS的聚合增强型煤矸石视频识别模型的平均精度均值优于其他模型，为77.12%；模型视频目标运动速度与识别精度呈负相关，基于LSS的聚合增强型煤矸石视频识别模型在慢速运动的目标检测上识别精度最高达83.82%。
- 煤矸石视频识别 /
- 视频帧多帧聚合 /
- 关键帧 /
- 非关键帧 /
- 注意力机制 /
- 时空关系网络 /
- 长短期存储
Abstract: Some key targets will be missed when using coal-gangue image recognition technology to recognize coal-gangue. Compared with the image target recognition model, the video target recognition model is closer to the requirements of the coal-gangue recognition and separation scene. The coal-gangue features in the video data can be extracted more widely and deeply. However, the influence of frame repetition, frame similarity and contingency of key frame on the model performance is not considered in the current coal-gangue video target recognition technology. In order to solve the above problems, this paper proposes an aggregation enhanced coal-gangue video recognition model based on long and short-term storage (LSS) model. Firstly, the key frames and non-key frames are used to screen the massive information. Multi-frame aggregation is carried out on the video frame sequence of the coal-gangue. The feature information of the key frame and the adjacent frame is aggregated through temporal relation networks (TRN), and a long-term video frame and a short-term video frame are established. The calculation amount of the model is reduced while the key feature information is not lost. Secondly, the feature weights among the long-term video frames, the short-term video frames and the keyframes are reallocated by using an attention mechanism that integrates semantic similarity weights, learnable weights and region of interest (ROI) similarity weights. Finally, the LSS module is designed to store the effective features of long-term video frames and short-term video frames. The module fuses them in the key frame recognition to enhance the characterization capability of the key frame features, so as to realize coal-gangue recognition. The model is tested based on the coal-gangue video data set in Zaoquan Coal Preparation Plant. The results show that in comparison with the memory enhanced global-local aggregation (MEGA) network, the flow-guided feature aggregation for video object detection (FGFA), the relation distillation networks (RDN) and deep feature flow for video recognition (DFF) model for video recognition, the mean average precision of the aggregation enhanced coal-gangue video recognition model based on LSS is 77.12 % and better than that of other models. The recognition precision of the modes is negatively correlated with the moving speed of the target in the video. The recognition precision of the model in this paper is 83.82% for the slow-moving target detection, and the performance is the best.
- video recognition of coal-gangue /
- multi-frame aggregation of video frames /
- key frame /
- non-key frame /
- attention mechanism /
- temporal relation networks /
- long and short-term storage

HTML全文

图 1 煤矸石视频中的关键帧与非关键帧

Figure 1. Key frames and non-key frames in coal-gangue video

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图 2 关键帧选取框架

Figure 2. Key frame selection frame

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图 3 基于LSS的聚合增强型煤矸石视频识别模型

Figure 3. Aggregation enhanced coal-gangue video recognition model based on LSS

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图 4 TRN特征融合过程

Figure 4. The TRN feature fusion process

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图 5 注意力机制计算原理

Figure 5. The tational principle of attention mechanism

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图 6 LSS模块设计原理

Figure 6. The LSS module design principle

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图 7 损失函数曲线

Figure 7. Loss function curve

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表 1 本文模型与MEGA，FGFA，RDN，DFF模型mAP对比

Table 1. The mAP comparison of the proposed model and MEGA，FGFA，RDN，DFF models %

模型	识别精度			mAP
模型	快速运动目标	中速运动目标	慢速运动目标	mAP
本文模型	55.12	76.02	83.82	77.12
MEGA-101	55.63	76.24	82.39	76.65
MEGA-50	49.53	70.58	79.83	72.63
RDN-101	51.65	71.95	82.10	74.68
RDN-50	45.27	67.46	80.22	70.40
FGFA-101	43.97	69.86	81.07	71.91
FGFA-50	40.75	66.57	78.90	68.68
DFF-101	37.47	66.87	79.32	68.42
DFF-50	35.65	62.19	74.14	63.50

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