ORIGINAL PAPER
Ore image target detection based on improved YOLOv5 network
1
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, Jiangxi, China
2
Jiangxi Province Engineering Research Center for Mechanical and Electrical of Mining and Metallurgy, Ganzhou 341000, Jiangxi, China
Submission date: 2024-10-14
Final revision date: 2025-01-13
Acceptance date: 2025-05-05
Publication date: 2025-06-11
Corresponding author
Gaipin Cai
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology
School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2025;41(2):177-196
KEYWORDS
TOPICS
Rational and efficient development and utilization of mineral resourcesEffective mineral deposits exploitationPreparation and processing of mineral raw materials
ABSTRACT
The existing target detection algorithms detect the ore on the conveyor belt after the crushing process with low precision and slow detection speed. This leads to challenges in achieving a balance between precision and speed, to enhance the detection precision and speed of ore, and in view of the problems of leakage, misdetection, and insufficient feature extraction of YOLOv5 in the task of ore image detection; this study presents a target detection approach relying on the CA attention mechanism (Coordinate attention for efficient mobile network design), the SIoU loss function and the target detection algorithm YOLOv5 combination of ore image particle target detection method. Integrating the CA attention mechanism into the YOLOv5 backbone feature network enhances the feature learning and extraction of ore images, thereby improving the precision of the detection model; the SIoU loss function is refined to boost the recognition precision of the network on ore images and address the shortcomings of the original loss function that fails to take angular loss, distance loss, and shape loss into account, thereby further improving the precision and speed of ore image detection. The experimental findings demonstrate that the AP value, value, and precision rate are improved compared with the pre-improved algorithm. The CA-YOLOv5 method is verified to be fast, effective, and advanced and provides a foundation for real-time target detection of ores on conveyor belts in subsequent intelligent mine production.
FUNDING
Supported by the National Natural Science Foundation of China under the project "Study on the damage evolution and dissociation mechanism of encapsulated mineral interface under multi-point symmetric ultrasonic loading" (52364025).
CONFLICT OF INTEREST
The Authors have no conflict of interest to declare.
METADATA IN OTHER LANGUAGES:
Polish
Wykrywanie celu obrazu rudy w oparciu o ulepszoną sieć YOLOv5
YOLOv5, wykrywanie obiektów, koordynacja uwagi dla wydajnego projektowania sieci mobilnych, ekstrakcja cech
Istniejące algorytmy wykrywania celu wykrywają rudę na taśmie przenośnika po procesie kruszenia z niską precyzją i niską szybkością wykrywania. Prowadzi to do wyzwań związanych z osiągnięciem równowagi między precyzją i szybkością, w celu zwiększenia precyzji i szybkości wykrywania rudy, a także ze względu na problemy z wyciekami, błędnym wykrywaniem i niewystarczającą ekstrakcją cech YOLOv5 w zadaniu wykrywania obrazu rudy; niniejsze badanie przedstawia podejście do wykrywania celu polegające na mechanizmie uwagi CA (Coordinate attention for efficient mobile network design), funkcji straty SIoU i kombinacji algorytmu wykrywania celu YOLOv5 w połączeniu z metodą wykrywania celu cząstek obrazu rudy. Zintegrowanie mechanizmu uwagi CA z siecią funkcji szkieletowych YOLOv5 usprawnia uczenie się funkcji i ekstrakcję obrazów rudy, tym samym zwiększając precyzję modelu wykrywania; funkcja straty SIoU została udoskonalona w celu zwiększenia precyzji rozpoznawania sieci na obrazach rudy i usunięcia niedociągnięć oryginalnej funkcji straty, która nie uwzględnia strat kątowych, strat odległości i strat kształtu, co jeszcze bardziej poprawia precyzję i szybkość wykrywania obrazów rudy. Wyniki eksperymentów pokazują, że wartość AP, wartość i wskaźnik precyzji są lepsze w porównaniu z wcześniej ulepszonym algorytmem. Metoda CA-YOLOv5 została zweryfikowana jako szybka, skuteczna i zaawansowana oraz stanowi podstawę do wykrywania celów rud na taśmach przenośnikowych w czasie rzeczywistym w późniejszej inteligentnej produkcji kopalnianej.
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| eISSN: | 2299-2324 |
| ISSN: | 0860-0953 |
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