Foreign object detection and counting method for belt conveyor based on improved YOLOv8n+DeepSORT
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摘要: 现有带式输送机异物检测方法存在提取目标语义信息能力弱、检测精度差等问题，且仅对异物进行识别检测，不能准确计算异物数量。针对该问题，设计了一种基于改进YOLOv8n+DeepSORT的带式输送机异物检测及计数方法。对YOLOv8n模型进行改进，再使用改进YOLOv8n（MSF−YOLOv8n）模型对带式输送机异物进行识别；将MSF−YOLOv8n模型的异物检测结果作为DeepSORT算法的输入，实现带式输送机异物跟踪和计数。YOLOv8n改进方法：使用C2f_MLCA模块替换主干网络中的C2f模块，提高网络在颜色信息单一环境下的信息提取能力；使用分离和增强注意力模块（SEAM）改进Head部分，以提高异物被遮挡情况下的检测精度；采用Focaler−IoU优化损失函数，解决检测目标形状差异大的问题。MSF−YOLOv8n模型性能验证实验结果表明，MSF−YOLOv8n模型的mAP50达93.2%，相较于基础模型提高了2.1%；参数量仅为2.82×106，比基础模型少了0.19×106，更适合部署到巡检机器人等边缘设备中；检测精度比YOLOv5s，YOLOv7，YOLOv8s算法分别高2.2%，1.3%，0.3%；其帧率虽然比YOLOv8s和YOLOv8n低，但仍可满足视频实时性检测要求。异物检测及计数实验结果表明，DeepSORT算法的准确率达80%，可准确跟踪被遮挡的锚杆及形状差异较大的目标。
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关键词:
- 带式输送机 /
- 目标检测与跟踪 /
- 异物检测及计数 /
- MSF−YOLOv8n /
- DeepSORT
Abstract: The existing foreign object detection methods for belt conveyors have problems such as weak capability to extract object semantic information, poor detection precision, and only recognizing and detecting foreign objects. The methods cannot accurately calculate the number of foreign objects. In order to solve the above problems, a foreign object detection and counting method for belt conveyors based on improved YOLOv8n+DeepSORT has been designed. The method improves the YOLOv8n model and then uses the improved YOLOv8n model to recognize foreign objects in belt conveyors. The method uses the foreign object detection results of the improved YOLOv8n model as input for the DeepSORT algorithm to achieve foreign object tracking and counting on belt conveyors. YOLOv8n improvement method is replacing the C2f module in the backbone network with the C2f_MLCA module to improve the network's information extraction capability in a single color information environment. The method improves the head section using the separated and enhancement attention module (SEAM) to enhance the detection precision of foreign objects when they are obstructed. The method uses Focaler IoU optimization loss function to solve the problem of large differences in the shape of detection objects. The performance verification experiment results of MSF-YOLOv8n model show that the mAP50 of MSF-YOLOv8n model reaches 93.2%, which is 2.1% higher than the basic model. The parameter count is only 2.82×106, which is 0.19×106 less than the basic model, making it more suitable for deployment in edge devices such as inspection robots. The detection precision is 2.2%, 1.3%, and 0.3% higher than YOLOv5s, YOLOv7, and YOLOv8s algorithms, respectively. Although its frame rate is lower than YOLOv8s and YOLOv8n, it still meets the requirements of real-time video detection. The results of foreign object detection and counting experiments show that the DeepSORT algorithm has an accuracy rate of 80% and can accurately track occluded anchor rods and objects with significant shape differences. -
图 4 基于MSF−YOLOv8n+DeepSORT的带式输送机异物检测及计数流程
Figure 4. Foreign object detection and counting process for belt conveyor based on MSF-YOLOv8n+DeepSORT
图 8 主流模型在测试集上的检测效果
Figure 8. The detection effect of mainstream models on the test set
图 9 不同工况下的异物检测结果
Figure 9. Foreign object detection results under different working conditions
表 1 MSF−YOLOv8n训练参数设置
Table 1. MSF-YOLOv8n training parameter setting
参数 数值 参数 数值 epochs 300 lr 0.01 batch 8 optimizer SGD imgsz 640 weight_decay 0.0005 workers 8 momentum 0.937 
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表 2 消融实验结果
Table 2. Ablation experiment results
基础
网络C2f_
MLCADetect_
SEAMFocaler−CIoU mAP50/% 参数
量/106个帧率/
（帧·s−1）√ × × × 91.1 3.01 115 √ √ × × 91.9 3.01 105 √ × √ × 92.7 2.82 93 √ × × √ 89.1 3.01 117 √ √ √ × 92.9 2.82 97 √ √ √ √ 93.2 2.82 101
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表 3 不同模型性能对比结果
Table 3. Comparison results of performance of different models
模型 mAP50/% 参数量/106个 帧率/（帧·s−1） YOLOv5s 91.0 9.12 91 YOLOv7 91.9 36.90 69.5 YOLOv8s 92.9 11.12 115 YOLOv8n 91.1 3.01 111 MSF−YOLOv8n 93.2 2.82 101
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表 4 带式输送机异物计数结果
Table 4. Foreign object counting results of belt conveyor
算法 人工计数结果 模型计数结果 差值 准确率/% BYTETracker 10 17 7 30 HybridSORT 10 14 4 60 BoT−SORT 10 15 5 50 DeepSORT 10 12 2 80
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