Conveyor Belt Damages Detection Based on Improved YOLOv8 Algorithm

doi:10.3969/j.issn.1004-132X.2025.12.003

Abstract

Abstract:

A conveyor belt damage detection algorithm was proposed based on improved YOLOv8 algorithm. Focal Modulation module was used to replace the original spatial pyramid pooling-fast（SPPF） module. In view of the high similarity between the damage and the background， a DySample dynamic up-sampling module was introduced to make the sampling points concentrate in the target areas and ignore the background parts， so as to achieve effective damage recognition. The efficient multi-scale attention（EMA） module was added to the neck network to obtain more detailed information and further improve the attention of injury targets. The PIoU v2 loss function was introduced to calculate the overlap areas between the real frame and the predicted frame， and the damage location was more accurate. Considering the aspect ratio might better adapt to the damage of different shapes. Experimental results show that the accuracy and mean average accuracy of the improved model for conveyor belt damage detection reach 90.3% and 93.2% respectively， which are 2.3% and 2.5% higher than the baseline model YOLOv8. The improved detection speed of YOLOv8 algorithm may reach 83 frames /s， which may fully meet the needs of real-time detection of conveyor belt damages.

Key words: belt damage, Focal Modulation module, efficient multi-scale attention module, YOLOv8 algorithm, DySample module, PIoU v2 loss function

摘要：

提出一种基于改进YOLOv8算法的输送带损伤检测算法：用Focal Modulation模块替换YOLOv8原有的SPPF模块；针对损伤与背景相似度高的问题，引入DySample轻量动态上采样模块，使采样点集中在目标区域而忽略背景部分，实现损伤的有效识别；在颈部网络中加入高效多尺度注意力模块来获取更多细节信息，进一步提高损伤目标的关注度。引入PIoU v2损失函数，通过计算真实框与预测框之间的重叠面积精准定位损伤，同时考虑长宽比以更好地适应不同形状损伤。实验结果表明，改进后的模型对输送带损伤检测的精确度和平均精确度均值分别达到了90.3%和93.2%，相比于基线模型YOLOv8提高了2.3%和2.5%。改进YOLOv8的检测速度达 83帧/s，可充分满足输送带损伤实时检测的需求。

关键词: 输送带损伤, Focal Modulation模块, 高效多尺度注意力模块, YOLOv8算法, DySample模块, PIoU v2损失函数

CLC Number:

TP391.4

Yuan YUAN, Yichao BAI, Lidong ZHOU, Wenjun MENG, Miao WANG, Wenbin QU. Conveyor Belt Damages Detection Based on Improved YOLOv8 Algorithm[J]. China Mechanical Engineering, 2025, 36(12): 2829-2836.

袁媛, 白一超, 周利东, 孟文俊, 王淼, 曲文斌. 改进YOLOv8的输送带损伤检测方法[J]. 中国机械工程, 2025, 36(12): 2829-2836.

Figures/Tables 15

Fig.1 FDEP-YOLOv8 network structure

Fig.2 Experimental photograph

Fig.3 Partially enhanced image

Tab.1 Experimental environment for model training

配置名称	版本/ 参数
深度学习框架	PyTorch 2.3.1+cu118
操作系统	Windows 11
CPU	Intel（R）Core（TM）i7-13650HX CPU@2.60GHz
GPU	NVIDIA GeForce GTX 4060Ti
CUDA	12.4
编译器	Python3.9.19
内存	24 GB

Fig.4 The improved model is compared with the similar model mean average precision

Fig.5 Improved accuracy comparison with similar models

Fig.6 Comparison of prediction effect between improved model and baseline model YOLOv8

Tab.2 SPPF module optimization comparison

模块类型	平均精度P_A/%				精确度P/%	平均精度均值P_ma/%	计算量/GFLOPS
模块类型	a	b	c	d	精确度P/%	平均精度均值P_ma/%	计算量/GFLOPS
SPPF	90.6	74.0	99.1	99.3	88	90.7	8.1
SPP	91.5	76.0	98.6	99.5	88.9	91.4	7.5
Focal Modulation	86.7	85.0	98.5	99.4	89.3	92.4	8.2
Basicrfb	88.3	82.7	98.8	99.5	91.6	92.3	7.6
SimSPPF	84.4	75.7	98.6	99.5	87.1	89.5	7.5

Tab.3 Comparison of experimental results of attention mechanism

模块类型	平均精度P_A/%				精确度P/%	平均精度均值P_ma/%	计算量/GFLOPS
模块类型	a	b	c	d	精确度P/%	平均精度均值P_ma/%	计算量/GFLOPS
YOLOv8	90.6	74.0	99.1	99.3	88.0	90.7	8.1
CBAM	88.8	77.8	98.7	99.2	91.2	91.1	8.1
COTA	91.0	75.3	99.1	99.3	87.4	91.2	8.5
Double attention	85.2	82.6	97.1	99.5	87.5	91.1	8.1
EMA	88.2	81.1	98.7	99.5	86.7	91.9	8.1

Fig.7 Loss function boundary frame loss and classification loss

Fig.8 Comparison of recall rates of sampling modules

Table 4 Ablation results

基础网络	$a'$	$b'$	$c'$	$d'$	精确度P/%	平均精度均值P_ma/%	计算量/GFLOPS
√	×	×	×	×	88.0	90.7	8.1
√	√	×	×	×	86.7	91.9	8.1
√	√	√	×	×	88.6	92.0	8.1
√	√	√	√	×	88.6	93.1	8.2
√	√	√	√	√	90.3	93.2	8.2

Table 4 Ablation results

基础网络	$a'$	$b'$	$c'$	$d'$	精确度P/%	平均精度均值P_ma/%	计算量/GFLOPS
√	×	×	×	×	88.0	90.7	8.1
√	√	×	×	×	86.7	91.9	8.1
√	√	√	×	×	88.6	92.0	8.1
√	√	√	√	×	88.6	93.1	8.2
√	√	√	√	√	90.3	93.2	8.2

Table 5 Comparison results of mainstream algorithms

算法	平均精度P_A/%				精确度 P/%	平均精度均值 P_ma/%	计算量/ GFLOPS
算法	a	b	c	d	精确度 P/%	平均精度均值 P_ma/%	计算量/ GFLOPS
YOLOV3-tiny	71.1	78.7	92.3	99.3	81.8	85.3	18.9
YOLOV5	86.3	79.3	98.2	99.3	88.3	90.8	7.1
YOLOV6	78.6	82.6	98.6	99.5	85.2	89.8	11.8
YOLOV8	90.6	74	99.1	99.3	88	90.7	8.1
YOLOV8s	89.6	79.9	97.2	99.5	90.6	91.5	28.4
YOLOV9t	83.9	83.3	99.5	99.5	86.2	91.6	7.6
YOLOV9s	92.5	78.2	99.5	99.5	91.7	92.4	26.7
YOLOV10n	87.2	77.2	97.5	98.1	87	90.0	8.2
FDEP-YOLOv8	93.6	80.4	99.4	99.4	90.3	93.2	8.2

Fig.9 Grad-CAM thermal map

Fig.10 Conveyor belt damage detection system interface

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