Conveyor belt damage detection method based on improved YOLOv4
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摘要: 针对现有输送带损伤检测方法检测精度低、检测速度慢且缺少对面积较小损伤检测的问题，提出了一种基于改进YOLOv4的输送带损伤检测方法。该方法以YOLOv4为基础，对PANet路径融合网络部分进行改进，增加与浅层特征层的融合，将原3个尺度的特征层融合增加到4个尺度，提高模型对输送带损伤的特征提取能力，提高检测精度；将PANet部分每个特征层融合后的卷积次数由5次减少到3次，减少计算量，提高检测速度；对输送带损伤图像进行标注，并输入改进的YOLOv4模型进行训练和测试。实验结果表明，基于改进YOLOv4的输送带损伤检测方法损失收敛速度快，模型训练效果好；基于改进YOLOv4的输送带损伤检测方法对输送带撕裂、表面磨损和表面缺陷检测的平均精度均值达96.86%，检测速度达20.66帧/s，与YOLOv4，YOLOv3和Faster-RCNN相比，平均精度均值分别提升了1.4%，6.35%，2.16%，检测速度分别提升了2.39，2.34，15.25帧/s；与YOLOv4相比，基于改进YOLOv4的输送带损伤检测方法检测精度更高，对面积较小损伤的检测效果更好。Abstract: In order to solve the problems of low detection precision, slow detection speed and lack of damage detection for small areas in existing conveyor belt damage detection methods, a conveyor belt damage detection method based on improved YOLOv4 is proposed.Based on YOLOv4, this method improves the PANet path fusion network part, increases the fusion with the shallow characteristic layer, increases the fusion of the original 3 scales of the characteristic layer to 4 scales, improves the characteristic extraction capability of the model for conveyor belt damage, and improves detection precision.The number of convolutions after fusion of each characteristic layer in the PANet part is reduced from 5 to 3 so as to reduce the amount of calculation and improve the detection speed.The conveyor belt damage images are labeled and input into the improved YOLOv4 model for training and testing.The experimental results show that the conveyor belt damage detection method based on improved YOLOv4 has a fast loss convergence speed and has a good model training effect.Based on improved YOLOv4 conveyor belt damage detection method, the average precision of the conveyor belt tear, surface wear and surface defect detection has reached 96.86%, and the detection speed has reached 20.66 frames/s.Compared with YOLOv4, YOLOv3 and Faster-RCNN, the average precision has increased by 1.4%, 6.35% and 2.16% respectively, and the detection speed has increased by 2.39, 2.34 and 15.25 frames/s respectively.Compared with YOLOv4, the conveyor belt damage detection method based on improved YOLOv4 has higher detection precision and better detection effect for small areas damages.
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Keywords:
- belt conveyor /
- conveyor belt damage detection /
- YOLOv4 /
- deep learning /
- PANet /
- characteristic layer fusion
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