Coal and gangue recognition research based on improved YOLOv5
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摘要: 现有基于深度学习的煤矸识别方法应用于井下复杂环境中时易出现误检和漏检情况，且对小目标煤矸的识别精度低。针对该问题，提出一种改进YOLOv5模型，并基于该模型实现煤矸识别。对采集的煤与矸石数据进行数据增强，以丰富数据集，提高数据利用率；在空间金字塔池化（SPP）模块中引入空洞卷积和残差块，得到残差ASPP模块，可在不损失图像信息的前提下，增大卷积输出感受野，强化模型对深层特征的提取；采用AdaBelief优化算法代替YOLOv5原有的Adam优化算法，提高模型的收敛速度与识别精度。实验结果表明：AdaBelief优化算法和残差ASPP模块可有效提高YOLOv5模型的精确率、召回率和平均精度均值（mAP）；改进YOLOv5模型的mAP达到94.43%，比原始YOLOv5模型提高了2.27%，帧率降低了0.03 帧/s，性能优于SSD，Faster R−CNN，YOLOv3，YOLOv4等主流目标检测模型；在极端黑暗的环境中，改进YOLOv5模型也能准确划定目标边界，识别效果优于其他改进YOLOv5模型。Abstract: The existing deep learning-based coal and gangue recognition methods are prone to false detection and missed detection when applied to underground complex environments. The recognition precision of small target coal and gangue is low. In order to solve this problem, an improved YOLOv5 model is proposed, and coal and gangue recognition is realized based on that model. Data enhancement is carried out on the collected coal and gangue data to enrich the data set and improve the data utilization rate. The atrous convolution and residual block are introduced into the spatial pyramid pooling (SPP) module to obtain the residual ASPP module. On the premise of not losing image information, the convolution output receptive field can be increased to enhance the extraction of deep features from the model. The AdaBelief optimization algorithm is used to replace the original Adam optimization algorithm of YOLOv5 to improve the convergence speed and recognition precision of the model. The experimental results show that the AdaBelief optimization algorithm and residual ASPP module can effectively improve the precision, recall rate and mean average precision (mAP) of the YOLOv5 model. The mAP of the improved YOLOv5 model reaches 94.43%, which is 2.27% higher than that of original YOLOv5 model. The frame rate is reduced by 0.03 frames/s. The performance of the improved YOLOv5 model is superior to SSD, Faster R-CNN, YOLOv3, YOLOv4 and other mainstream target detection models. In extremely dark environments, the improved YOLOv5 model can also accurately delineate the target boundary, and the recognition effect is better than other improved YOLOv5 models.
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图 4 基于AdaBelief优化算法的YOLOv5模型训练过程
Figure 4. YOLOv5 model training process based on AdaBelief optimization algorithm
表 1 优化算法对比实验结果
Table 1. Comparison of experimental results of optimization algorithms
算法 精确率/% 召回率/% mAP/% 帧率/(帧·s−1) Adam 90.74 93.11 91.68 16.33 SGDM 88.83 91.95 90.92 16.79 AdaBelief 91.07 94.16 92.07 16.31 
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表 2 采用不同空洞率时的实验结果
Table 2. Experimental results with different void ratios
空洞率组合 精确率/% 召回率/% mAP/% 无 89.61 94.12 90.85 [6，12，18，24] 87.35 88.39 87.99 [1，3，5，7] 91.02 95.18 91.75 [2，3，7，13] 90.19 94.33 91.03 [1，2，5，9] 91.36 96.64 92.46
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表 3 特征提取模块对比实验结果
Table 3. Comparison of experimental results of feature extraction modules
特征提取
模块精确率/% 召回率/% mAP/% 帧率/(帧·s−1) SPP 90.11 95.19 90.89 16.21 ASPP 91.36 96.54 91.98 16.13 残差ASPP 91.92 96.95 92.25 16.25
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表 4 主流目标检测模型性能对比
Table 4. Performance comparison of mainstream target detection models
模型 mAP/% 帧率/(帧·s−1) SSD 81.81 16.58 Faster R−CNN 83.53 11.91 YOLOv3 86.69 16.44 YOLOv4 91.35 15.93 原始YOLOv5 92.16 15.65 改进YOLOv5 94.43 15.62
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