| 叶志宇,贾晓芬,王天奇.面向煤矸识别的目标检测算法[J].电子测量与仪器学报,2024,38(8):145-152 |
| 面向煤矸识别的目标检测算法 |
| Target detection algorithm for coal and gangue identification |
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| DOI： |
| 中文关键词: 煤矸检测 目标识别 Eswish激活函数 注意力机制 BiFPN结构 |
| 英文关键词:coal gangue detection target recognition Eswish activation function attention mechanism BifFPN structure |
| 基金项目:国家自然科学基金面上项目(52174141)、安徽省自然科学基金面上项目(2108085ME158)、安徽高校协同创新项目(GXXT-2020-54)资助 |
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| Author | Institution |
| Ye Zhiyu | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001,China |
| Jia Xiaofen | 1.School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001,China;
2.School of Artificial Intelligence, Anhui University of Science and Technology, Huainan 232001,China |
| Wang Tianqi | School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001,China |
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| 摘要点击次数: 24 |
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| 中文摘要: |
| 煤与矸石具有目标密集、特征差异小等特征，基于图像处理的识别方法普遍存在检测速度慢、精度低等问题。为了进一步提高煤矸检测的速度和精度，提出一种GE YOLOv5s煤矸检测模型。首先在YOLOv5s的基础上引入Ghost Conv代替卷积操作，并设计新模块GhostCSP，在提升模型检测速度的同时实现网络的轻量化；其次在预测层中加入GC自注意力机制，融合SENet的轻量化和NLNet长距离信息全局捕获的优势，使网络记忆、放大煤矸图像间的细微差异特征，提升模型的表现力；然后在Neck部分采用双向特征金字塔网络（BiFPN）结构，利用BiFPN融合3个不同维度的特征，通过加权特征融合机制提高模型计算效率，进一步提升煤矸检测速度；最后设计一种新型激活函数Eswish替代SiLU激活函数，提高参数利用率的同时加快模型收敛速度并提升鲁棒性。实验数据表明：相较于YOLOv5s模型减少了34.1%的参数量和38.6%的浮点运算量，并且在mAP 0.5：0.95指标上提升了1.9%。对比实验显示，相较于YOLOv3、SSD、FasterR-CNN和YOLOv5-scSE的mAP 0.5：0.95指标分别提高了16.6%、4.8%、13.6%和3.8%。将GE-YOLOv5s模型应用于煤矸石目标检测过程中，具有更优的识别性能、鲁棒性、网络泛化能力，可有效避免漏检、误检和重叠现象。 |
| 英文摘要: |
| Coal and gangue have the characteristics of dense targets and small feature differences, and the recognition methods based on image processing generally have the problems of slow detection speed and low accuracy. To further improve the speed and accuracy of coal gangue detection, a GE-YOLOv5s coal gangue detection model is proposed. Firstly, Ghost Conv is introduced based on YOLOv5s instead of convolution operation, and a new module GhostCSP is designed to improve the detection speed of the model while realizing the lightweight of the network; secondly, the GC self-attention mechanism is added in the prediction layer, which integrates the lightweight of SENet and the advantage of global capture of long-distance information of NLNet, to enable the network to memorize and magnify the Then in the Neck part, a bidirectional feature pyramid network (BiFPN) structure is adopted, and BiFPN is used to fuse the features of three different dimensions to improve the computational efficiency of the model through the weighted feature fusion mechanism to further enhance the speed of coal gangue detection; finally, a new type of activation function is designed to replace the activation function of SiLU, which can improve the utilization rate and accelerate the convergence of the model. Finally, a new activation function Eswish is designed to replace the SiLU activation function, which improves the parameter utilization rate, accelerates the convergence speed of the model and improves the robustness. The experimental data show that compared with the YOLOv5s model, the number of parameters is reduced by 34.1% the amount of floating-point operations is reduced by 38.6%, and the mAP 0.5:0.95 index is improved by 1.9%. Comparison experiments show that the mAP 0.5:0.95 metric is improved by 16.6%, 4.8%, 13.6% and 3.8% compared to YOLOv3, SSD, FasterR-CNN, and YOLOv5-scSE, respectively. Applying the GE-YOLOv5s model to the gangue target detection process has better recognition performance, robustness, and network generalization ability, and can effectively avoid the phenomena of leakage, misdetection and overlapping. |
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