Research on an Image Recognition Automatic Counting System Based on Improved YOLOv8

This paper proposes a deep learning-based automatic target counting system for industrial environments. The system features enhanced YOLOv8 for small target detection using attention mechanisms and optimized training strategies. It also customizes the DeepSort algorithm for improved robustness in in...

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Bibliographic Details
Published inIEEE International Conference on Power, Intelligent Computing and Systems (Online) pp. 1417 - 1421
Main Authors Chen, Jiaqi, Zhao, Jiaxi, Zhang, Deyong, Ye, Zhimin, Liu, Jianyou, Huang, Wen
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.07.2024
Subjects
Accuracy
Automatic counting
Industrial automation
Logistics
Object detection
Production
Real-time systems
Robustness
Target detection
Target tracking
Testing
Training
Warehousing
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ISSN2834-8567
DOI10.1109/ICPICS62053.2024.10795929

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Summary:This paper proposes a deep learning-based automatic target counting system for industrial environments. The system features enhanced YOLOv8 for small target detection using attention mechanisms and optimized training strategies. It also customizes the DeepSort algorithm for improved robustness in industrial multi-object tracking. Utilizing GPU acceleration, the system achieves real-time response within 0.2 seconds. Testing with simulated and real data shows a detection mAP of 88.7%, a tracking MOTA of 91.5%, and a counting accuracy of 96.8%. This system meets industrial requirements for automation, real-time performance, and reliability, offering promising application prospects.
ISSN:2834-8567
DOI:10.1109/ICPICS62053.2024.10795929