Flame smoke detection algorithm based on YOLOv5 in petrochemical plant

Yang, Yueting; Shou-song, HU; Ke, Ye; Zhou, Runguan

doi:10.1108/ijicc-11-2022-0291

Cited by 5 publications

(3 citation statements)

References 35 publications

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“…The YOLO models have shown fascinating results in many downstream tasks (Wang et al. , 2022b; Yang et al. , 2023).…”

Section: Methodsmentioning

confidence: 99%

“…The Tiny YOLOv7 is a recent addition to the series of Tiny YOLO object detectors proposed in (Wang et al, 2022a) and is publicly available on the DarkNet repository (Alexey, 2020). The YOLO models have shown fascinating results in many downstream tasks (Wang et al, 2022b;Yang et al, 2023). The Tiny YOLOv7 is made up of 72 convolutional layers and three detection heads, as shown in Figure 2.…”

Section: Proposed Eye-yolomentioning

confidence: 99%

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EYE-YOLO: a multi-spatial pyramid pooling and Focal-EIOU loss inspired tiny YOLOv7 for fundus eye disease detection

Kumar,

Dhanalakshmi

2024

IJICC

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PurposeThe purpose of this work is to present an approach for autonomous detection of eye disease in fundus images. Furthermore, this work presents an improved variant of the Tiny YOLOv7 model developed specifically for eye disease detection. The model proposed in this work is a highly useful tool for the development of applications for autonomous detection of eye diseases in fundus images that can help and assist ophthalmologists.Design/methodology/approachThe approach adopted to carry out this work is twofold. Firstly, a richly annotated dataset consisting of eye disease classes, namely, cataract, glaucoma, retinal disease and normal eye, was created. Secondly, an improved variant of the Tiny YOLOv7 model was developed and proposed as EYE-YOLO. The proposed EYE-YOLO model has been developed by integrating multi-spatial pyramid pooling in the feature extraction network and Focal-EIOU loss in the detection network of the Tiny YOLOv7 model. Moreover, at run time, the mosaic augmentation strategy has been utilized with the proposed model to achieve benchmark results. Further, evaluations have been carried out for performance metrics, namely, precision, recall, F1 Score, average precision (AP) and mean average precision (mAP).FindingsThe proposed EYE-YOLO achieved 28% higher precision, 18% higher recall, 24% higher F1 Score and 30.81% higher mAP than the Tiny YOLOv7 model. Moreover, in terms of AP for each class of the employed dataset, it achieved 9.74% higher AP for cataract, 27.73% higher AP for glaucoma, 72.50% higher AP for retina disease and 13.26% higher AP for normal eye. In comparison to the state-of-the-art Tiny YOLOv5, Tiny YOLOv6 and Tiny YOLOv8 models, the proposed EYE-YOLO achieved 6–23.32% higher mAP.Originality/valueThis work addresses the problem of eye disease recognition as a bounding box regression and detection problem. Whereas, the work in the related research is largely based on eye disease classification. The other highlight of this work is to propose a richly annotated dataset for different eye diseases useful for training deep learning-based object detectors. The major highlight of this work lies in the proposal of an improved variant of the Tiny YOLOv7 model focusing on eye disease detection. The proposed modifications in the Tiny YOLOv7 aided the proposed model in achieving better results as compared to the state-of-the-art Tiny YOLOv8 and YOLOv8 Nano.

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“…The YOLO models have shown fascinating results in many downstream tasks (Wang et al. , 2022b; Yang et al. , 2023).…”

Section: Methodsmentioning

confidence: 99%

Section: Proposed Eye-yolomentioning

confidence: 99%

EYE-YOLO: a multi-spatial pyramid pooling and Focal-EIOU loss inspired tiny YOLOv7 for fundus eye disease detection

Kumar,

Dhanalakshmi

2024

IJICC

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“…YOLOv5 uses the GIOU_Loss loss function instead of the squared loss function. GIOU_Loss improves on the method of calculating the IOU (intersection and concurrency ratio), that is, the ratio of the area of the target frame and the real frame overlapping to the total area [7] , by taking into account that the predicted frame A and the real frame B do not overlap, and adding a frame that can include Box C of the predicted and real frames, which is calculated as in equation ( 2) :…”

Section: Return Loss Calculationmentioning

confidence: 99%

Recognition and prediction of SMT solder joint defects based on YOLOV5

Ma,

Li,

Liu

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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PCB (Printed circuit board) is an important electronic component. It is also the carrier of electronic components and metal wires. With the rapid development of science and technology, the production of PCB board has become more refined and the structure has become more complex. At the same time, the appearance of SMT technology has greatly improved the integration of the circuit board, but SMT technology also has defects, such as "monument", "tin bead", "bridge", etc., which leads to short circuit fracture. Based on the research of solder joint defect detection based on YOLOV5, this paper designs an automatic recognition and prediction system for SMT solder joint defects based on YOLOV5 to solve the problems of low accuracy, slow speed and high cost of current solder joint defect detection technology. Real-time image preprocessing was performed on the collected images, and the possible steles, Wuxi, multi-tin, bridge and broken holes were detected. Defect detection in the system is divided into three main parts: image processing, motion control and human-computer interaction. The system has the advantages of simple operation, accurate detection and fast detection speed.

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