ORIGINAL PAPER
Research on Coal Gangue Detection and Recognition Based on Lightweight Network MS-YOLOV3
1
Anhui University of Science and Technology
2
China Coal Technology Engineering Group Coal Mining Research Institute
Submission date: 2022-08-16
Final revision date: 2022-10-25
Acceptance date: 2022-11-10
Publication date: 2022-12-20
Corresponding author
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2022;38(4):133-152
KEYWORDS
TOPICS
Optimization of resources and reserves managementRational and efficient development and utilization of mineral resourcesPreparation and processing of mineral raw materialsUtilization of recycled mineral raw materials and substitutes
ABSTRACT
The rapid and accurate detection and identification of coal gangue is one of the premises and key technologies of the intelligent separation of coal gangue, which is of considerable importance for the separation of coal gangue. Focusing on the problems in the current deep learning algorithms for the detection and recognition of coal gangue, such as large model memory and slow detection speed, a rapid detection method for lightweight coal gangue is proposed. YOLOv3 is taken as the basic structure and improved. The MobileNetv2 lightweight feature extraction network is selected to replace Darknet53 as the main network of the detection algorithm to improve the detection speed. Spatial pyramid pooling (SPP) is added after the backbone network to convert different feature maps into fixed feature maps in order to improve the positioning accuracy and detection capability of the algorithm, thereby obtaining the lightweight network MS-YOLOV3. The experimental equipment was set up and multi-condition coal and gangue datasets were constructed. The model was trained and the identification and positioning results of the model were tested under different sizes, illumination intensities and various working conditions, and compared with other algorithms. Experimental results show that the proposed algorithm can detect the coal gangue quickly and accurately, with an mAP of 99.08%, a speed of 139 fps and a memory occupation of only 9.2 M. In addition, the algorithm can effectively detect mutually stacking coal and gangue of different quantities and sizes under different lights with high confidence and with a certain degree of environmental robustness and practicability. Compared with the YOLOv3, the performance of the proposed algorithm is significantly improved. Under the premise that the accuracy is unchanged, the FPS increases by 127.9% and the memory decreases by 96.2%. Therefore, the MS-YOLOv3 algorithm has the advantages of small memory, high accuracy and fast speed, which can provide online technical support for the detection and identification of coal and gangue.
ACKNOWLEDGEMENTS
This research was funded by Anhui University Graduate scientific research project (No. YJS20210371), the National Natural Science Fund Project (No. 51974004), Collaborative Innovation Project of Collaborative Tackling of Universities in Anhui Province (No. GXXT-2020-060), China Postdoctoral Science Foundation (No. 2019M662133), Open Foundation of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mine (No. SKLMRDPC20KF10), Collaborative Innovation Project of Collaborative Tackling of Universities in Anhui Province
(No. GXXT-2020-054). Science and Technology Innovation Fund of Coal Mining and Designing Department of Tiandi Science and Technology Co., Ltd (KJ-2021-KCMS-04).
METADATA IN OTHER LANGUAGES:
Polish
Badania nad wykrywaniem i rozpoznawaniem skały płonnej w oparciu o lekką sieć MS-YOLOV3
wykrywanie i rozpoznawanie, węgiel i skała płonna, MobileNetv2, SPP, YOLOv3
Szybkie i dokładne wykrywanie oraz identyfikacja skały płonnej jest jedną z przesłanek i kluczowych technologii inteligentnej separacji skały płonnej. Koncentrując się na problemach związanych z obecnymi algorytmami wykrywania i rozpoznawania skały płonnej z głębokim uczeniem, takimi jak duża pamięć modelu i niska prędkość wykrywania, zaproponowano metodę szybkiego wykrywania lekkiej skały płonnej. YOLOv3 jest traktowany jako struktura podstawowa i ulepszony. Lekka sieć ekstrakcji funkcji Mobilenetv2 została wybrana w celu zastąpienia Darknet53 jako głównej sieci algorytmu wykrywania w celu poprawy szybkości wykrywania. Spatial Pyramid Pooling (SPP) jest dodawany po sieci szkieletowej w celu konwersji różnych map obiektów na mapy stałych funkcji, aby poprawić dokładność pozycjonowania i zdolność wykrywania algorytmu, uzyskując w ten sposób lekką sieć MS-YOLOV3. Ustawiono sprzęt eksperymentalny i skonstruowano wielowarunkowe zbiory danych dotyczące węgla i skały płonnej. Model został przeszkolony, a wyniki identyfikacji i pozycjonowania modelu zostały przetestowane przy różnych rozmiarach, natężeniu oświetlenia i różnych warunkach pracy oraz porównane z innymi algorytmami. Wyniki eksperymentu pokazują, że zaproponowany algorytm jest w stanie szybko i dokładnie wykryć skałę węglową, z mAP na poziomie 99,08%, szybkością 139 fps i zajęciem pamięci zaledwie 9,2 MB. Ponadto może skutecznie wykrywać różne światła, różne rozmiary, wzajemne układanie w stosy oraz wielokrotną ilość węgla i skały płonnej, z dużą pewnością i pewną odpornością środowiskową i wykonalnością. W porównaniu z YOLOv3 wydajność proponowanego algorytmu jest znacznie lepsza. Przy założeniu, że dokładność pozostaje w zasadzie niezmieniona, FPS wzrasta o 127,9%, a pamięć spada o 96,2%. Dlatego algorytm MS-YOLOv3 ma zalety małej pamięci, wysokiej dokładności i dużej szybkości, co może zapewnić wsparcie techniczne dla wykrywania i identyfikacji węgla i skały płonnej online.
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| eISSN: | 2299-2324 |
| ISSN: | 0860-0953 |
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