A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5
Industry requires defect detection to ensure the quality and safety of products. In resource-constrained devices, real-time speed, accuracy, and computational efficiency are the most critical requirements for defect detection. This paper presents a novel approach for real-time detection of surface d...
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MDPI AG
2025-01-01
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| author | Burhan Duman |
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| description | Industry requires defect detection to ensure the quality and safety of products. In resource-constrained devices, real-time speed, accuracy, and computational efficiency are the most critical requirements for defect detection. This paper presents a novel approach for real-time detection of surface defects on LPG cylinders, utilising an enhanced YOLOv5 architecture referred to as GLDD-YOLOv5. The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network’s backbone. It also modifies the P3–P4 head structure to increase detection speed. These changes enable the model to focus more effectively on small and medium-sized defects. Based on comparative analysis with other YOLO models, the proposed method demonstrates superior performance. Compared to the base YOLOv5s model, the proposed method achieved a 4.6% increase in average accuracy, a 44% reduction in computational cost, a 45% decrease in parameter counts, and a 26% reduction in file size. In experimental evaluations on the RTX2080Ti, the model achieved an inference rate of 163.9 FPS with a total carbon footprint of 0.549 × 10<sup>−3</sup> gCO<sub>2</sub>e. The proposed technique offers an efficient and robust defect detection model with an eco-friendly solution compatible with edge computing devices. |
| format | Article |
| id | doaj-art-5e84df532d1148b69563c69810af2fdc |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-5e84df532d1148b69563c69810af2fdc2025-01-10T13:15:37ZengMDPI AGApplied Sciences2076-34172025-01-0115145810.3390/app15010458A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5Burhan Duman0Department of Computer Engineering, Faculty of Technology, Isparta University of Applied Sciences, 32100 Isparta, TurkeyIndustry requires defect detection to ensure the quality and safety of products. In resource-constrained devices, real-time speed, accuracy, and computational efficiency are the most critical requirements for defect detection. This paper presents a novel approach for real-time detection of surface defects on LPG cylinders, utilising an enhanced YOLOv5 architecture referred to as GLDD-YOLOv5. The architecture integrates ghost convolution and ECA blocks to improve feature extraction with less computational overhead in the network’s backbone. It also modifies the P3–P4 head structure to increase detection speed. These changes enable the model to focus more effectively on small and medium-sized defects. Based on comparative analysis with other YOLO models, the proposed method demonstrates superior performance. Compared to the base YOLOv5s model, the proposed method achieved a 4.6% increase in average accuracy, a 44% reduction in computational cost, a 45% decrease in parameter counts, and a 26% reduction in file size. In experimental evaluations on the RTX2080Ti, the model achieved an inference rate of 163.9 FPS with a total carbon footprint of 0.549 × 10<sup>−3</sup> gCO<sub>2</sub>e. The proposed technique offers an efficient and robust defect detection model with an eco-friendly solution compatible with edge computing devices.https://www.mdpi.com/2076-3417/15/1/458green deep learningreal-timedefect detectionYOLOlightweight networkLPG cylinder |
| spellingShingle | Burhan Duman A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 Applied Sciences green deep learning real-time defect detection YOLO lightweight network LPG cylinder |
| title | A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 |
| title_full | A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 |
| title_fullStr | A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 |
| title_full_unstemmed | A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 |
| title_short | A Real-Time Green and Lightweight Model for Detection of Liquefied Petroleum Gas Cylinder Surface Defects Based on YOLOv5 |
| title_sort | real time green and lightweight model for detection of liquefied petroleum gas cylinder surface defects based on yolov5 |
| topic | green deep learning real-time defect detection YOLO lightweight network LPG cylinder |
| url | https://www.mdpi.com/2076-3417/15/1/458 |
| work_keys_str_mv | AT burhanduman arealtimegreenandlightweightmodelfordetectionofliquefiedpetroleumgascylindersurfacedefectsbasedonyolov5 AT burhanduman realtimegreenandlightweightmodelfordetectionofliquefiedpetroleumgascylindersurfacedefectsbasedonyolov5 |