YOLOv5-light: efficient convolutional neural networks for flame detection

Qingpeng, Wang; Yan, Chunman; Zhang, Xiang

doi:10.1117/1.jei.31.6.063057

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…However, shallow visual features are susceptible to variations in imaging conditions and thus have limited robustness and generalization capabilities. 13 Deep NNs have demonstrated remarkable performance on a wide range of visual tasks, such as object detection, 14,15 object tracking, 16,17 segmentation, 18,19 and image classification. 13,[20][21][22] Deep convolutional neural networks (CNN) [23][24][25][26] and transformers [27][28][29][30][31] are two good examples.…”

Section: Image Recognition Techniquesmentioning

confidence: 99%

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TEG: image theme recognition using text-embedding-guided few-shot adaptation

Wang,

Lu,

Wang

et al. 2024

J. Electron. Imag.

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Grouping images into different themes is a challenging task in photo book curation. Unlike image object recognition, image theme recognition focuses on the understanding of the main subject or overall meaning conveyed by an image. However, it is challenging to achieve satisfactory performance using existing general image recognition methods. In this work, we aim to solve the image theme recognition task with few-shot training samples using pre-trained contrastive language-image models. A text-prompt-guided few-shot image adaptation framework is proposed, which incorporates a text-embedding-guided classifier and an auxiliary classification loss to exploit embedded visual and text features, stabilize the network training, and enhance recognition performance. We also present an annotated dataset Theme25 for studying image theme recognition. We conducted experiments on our Theme25 dataset as well as the publicly available CIFAR100 and ImageNet datasets to demonstrate the superiority of our method over the compared stateof-the-art methods.

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Section: Image Recognition Techniquesmentioning

confidence: 99%

“…Deep NNs have demonstrated remarkable performance on a wide range of visual tasks, such as object detection, 14 , 15 object tracking, 16 , 17 segmentation, 18 , 19 and image classification 13 , 20 – 22 Deep convolutional neural networks (CNN) 23 – 26 and transformers 27 – 31 are two good examples.…”

Section: Related Workmentioning

confidence: 99%

TEG: image theme recognition using text-embedding-guided few-shot adaptation

Wang,

Lu,

Wang

et al. 2024

J. Electron. Imag.

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“…Ruiqiang 24 introduced the attention mechanism to improve the YOLOv5s model, and established a lightweight crack detection model for bridge crack detection. Wang et al 25 proposed Ghost modules to improve the head network of YOLOv5 and reduce its computational cost. Guo et al 26 detected steel surface defect by introducing the MSFT-YOLO model.…”

Section: Defect Detection Based On Deep Learningmentioning

confidence: 99%

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

Ding,

Pan,

Zhang

et al. 2023

J. Electron. Imag.

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.Metal gaskets are an essential component of the manufacturing industry, and detecting end-face defects is critical to ensuring their quality. However, current detection methods struggle to detect such defects due to the gaskets’ uneven end-face structure and small defect size. To address such issues, an approach called SR-HorNetYOLO has been proposed. This approach combines traditional machine vision and deep learning techniques. The visual saliency method is used to simplify the image and remove irrelevant features. YOLOv5 is then optimized in three ways: the GhostNet backbone replaces the CSPDarkNet53 backbone to reduce model weight, the self-attention mechanism and bottleneck transformer block improve the precision of detecting small targets, and the feature pyramid is improved through the HorNet network to enhance the predictive performance of the network. Finally, a classification module and position module are employed to classify and locate three-fusion proportion characteristic images. The experimental results show that the SR-HorNetYOLO method achieves a precision of 93.3% and a recall rate of 94.0%.

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YOLOv5-CSF: an improved deep convolutional neural network for flame detection

et al. 2023

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The occurrence of fire threatens people's lives and property damage. An effective way to reduce flame damage is to detect flames in video images and take appropriate measures to contain fires at an early stage of occurrence. To solve the problem of low detection precision of current image-based flame detection methods, we propose a high precision flame detector based on yolov5 improvement. Coordinate Attention Blocks are introduced in the backbone network to obtain both channel relationships and long-range dependencies with precise positional information and increase the feature expression of the backbone network. Swin Transformer Blocks are introduced in the neck to expand the receptive field of the network and improve the flame feature extraction; the Adaptive Spatial Feature Fusion module is introduced in the head network to enhance the multi-scale feature fusion of flame features and reduce false alarms. Compared with the yolov5l, the precision of the model is improved by 4.1%. Compared with other existing detectors, it achieves the best average precision of 66.8%.

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YOLOv5-light: efficient convolutional neural networks for flame detection

Cited by 5 publications

References 23 publications

TEG: image theme recognition using text-embedding-guided few-shot adaptation

TEG: image theme recognition using text-embedding-guided few-shot adaptation

SR-HorNetYOLO: an efficient algorithm for defect detection of metal gasket end-face

YOLOv5-CSF: an improved deep convolutional neural network for flame detection

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