Improved YOLOv8 model-based object detection method for inspection robot
10.19745/j.1003-8868.2024041
- VernacularTitle:基于改进YOLOv8模型的巡检机器人目标检测方法研究
- Author:
Bei-Chen YIN
1
;
Zi-Jian WANG
;
Zhi CHENG
;
Xin-Xi XU
Author Information
1. 军事科学院系统工程研究院,天津 300161
- Keywords:
YOLOv8 model;
inspection robot;
object detection;
attention mechanism
- From:
Chinese Medical Equipment Journal
2024;45(3):1-8
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a object detection method based on an improved YOLOv8 model to solve the problems of the inspection robot in low accuracy for recognizing pointer-type or obscured meters.Methods Firstly,a YOLOv8 model was chosen as the foundation object detection model,based on which the coordinate attention(CA)mechanism was introduced to enhance the model's understanding of the spatial structure of the input data over long distances;secondly,the original complete IoU(CIoU)loss function was replaced by an efficient IoU(EIoU)loss function to accelerate the convergence of the model's detection frame;finally,the soft non-maximum suppression(Soft-NMS)function took the place of the traditional NMS method to suppress the redundant bounding box smoothly and further improve the detection accuracy.The improved YOLOv8 model(YOLOv8nxt model)was compared with the YOLOv8n model to verify its efficacy for object detection.Results The YOLOv8nxt model with a size of 6.2 M had the position loss decreased by 1.3％,mAP_0.5∶0.95 increased by 1.7％,detection accuracy raised by 0.87％and detection time prolonged by only 0.2 ms when comparted with the YOLOv8n model.Conclusion The improved YOLOv8 model-based object detection method enhances the accuracy and speed of the inspection robot's recognition of meters during movement,and can effectively solve the problems of the inspection robot in the object detection stage.[Chinese Medical Equipment Journal,2024,45(3):1-8]
