Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Li, Tingting; Zhao, Enting; Zhang, Junguo; Hu, Chunhe

doi:10.3390/electronics8101131

Cited by 42 publications

(29 citation statements)

References 34 publications

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“…Also, it has to be noted that this hybrid system model was deployed in a tire manufacturing unit, and it produced efficient results in automatically diagnosing the bubble-defects in treads and sidewalls of tires. In the future work, more advanced CNN enabled approaches can be implemented for automated detection of defects [26][27][28][29][30], thus ensuring and realizing a sustainable tire manufacturing process.…”

Section: Discussionmentioning

confidence: 99%

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

et al. 2019

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In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.Electronics 2020, 9, 45 2 of 13 be observed that enabling technologies for smart tire quality assessment for realizing intelligent tire manufacturing has been gaining prominence to address challenges such as automated tire defects detection.Generally, it can be witnessed that in the modern-day scenario, there is a humongous and swift growth in the gadgets, equipment, and devices connected to the internet. These devices, gadgets, and equipment have profound computing characteristics, and at the same time, they are exceedingly performance-oriented [2]. Due to all these facts, the concept of deep learning has evolved into a newer dimension, and it plays a significant part in processing and recognizing images, speech, and video, and so on. Furthermore, the implementation of a deep learning paradigm for automation in conventional manufacturing units significantly minimizes the usage of physical labor, and it also enhances the overall competence and efficacy of these units. Digital shearography is a laser-based measuring approach that relies on the processing of digital data, interferometry, and phase-shifting paradigm [3-5].A shearography system was developed by applying a spatial light modulator for controlling the amount of shearing and the direction of the phase light automatically and accurately. The system eliminates the nonlinear random error and enhances the efficiency of testing [6]. A system was developed using shearography to examine the exterior heatproof covering of a cylinder, and defect detection was done using an artificial intelligence-based recognition algorithm for deep...

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Section: Discussionmentioning

confidence: 99%

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

et al. 2019

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“…Zhang et al 2018;Q.X. Zhang 2018;Yuan et al 2018;Akhloufi et al 2018;Barmpoutis et al 2019;Jakubowski et al 2019;Sousa et al 2019;, T. Li et al 2019Muhammad et al 2018;Wang et al 2019). Of particular note, Q.X.…”

Section: Fire Detectionunclassified

A review of machine learning applications in wildfire science and management

et al. 2020

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Artiﬁcial intelligence has been applied in wildﬁre science and management since the 1990s, with early applications including neural networks and expert systems. Since then the ﬁeld has rapidly progressed congruently with the wide adoption of machine learning (ML) methods in the environmental sciences. Here, we present a scoping review of ML applications in wildﬁre science and management. Our overall objective is to improve awareness of ML methods among wildﬁre researchers and managers, as well as illustrate the diverse and challenging range of problems in wildﬁre science available to ML data scientists. To that end, we ﬁrst present an overview of popular ML approaches used in wildﬁre science to date, and then review the use of ML in wildﬁre science as broadly categorized into six problem domains, including: 1) fuels characterization, ﬁre detection, and mapping; 2) ﬁre weather and climate change; 3) ﬁre occurrence, susceptibility, and risk; 4) ﬁre behavior prediction; 5) ﬁre eﬀects; and 6) ﬁre management. Furthermore, we discuss the advantages and limitations of various ML approaches relating to data size, computational requirements, generalizability, and interpretability, as well as identify opportunities for future advances in the science and management of wildﬁres within a data science context. In total, we identfied 300 relevant publications up to the end of 2019, where the most frequently used ML methods across problem domains included random forests, MaxEnt, artiﬁcial neural networks, decision trees, support vector machines, and genetic algorithms. As such, there exists opportunities to apply more current ML methods — including deep learning and agent based learning — in the wildﬁre sciences, especially in instances involving very large multivariate datasets. We must recognize, however, that despite the ability of ML methods to learn on their own, expertise in wildﬁre science is necessary to ensure realistic modelling of ﬁre processes across multiple scales, while the complexity of some ML methods, such as deep learning, requires a dedicated and sophisticated knowledge of their application. Finally, we stress that the wildﬁre research and management communities play an active role in providing relevant, high quality, and freely available wildﬁre data for use by practitioners of ML methods.

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“…In convolutional neural networks, a model to detect wildfire smoke named wildfire smoke dilated dense net was proposed by Li et al [ 7 ], consisting of a candidate smoke region segmentation strategy using an advanced network architecture. Mangalathu et al [ 8 ] performed an evaluation of building clusters affected by earthquakes by exploring the deep learning method, which uses long short-term memory.…”

Section: Related Workmentioning

confidence: 99%

Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

Aamir

Ali

Irfan

et al. 2021

Sensors

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Natural disasters not only disturb the human ecological system but also destroy the properties and critical infrastructures of human societies and even lead to permanent change in the ecosystem. Disaster can be caused by naturally occurring events such as earthquakes, cyclones, floods, and wildfires. Many deep learning techniques have been applied by various researchers to detect and classify natural disasters to overcome losses in ecosystems, but detection of natural disasters still faces issues due to the complex and imbalanced structures of images. To tackle this problem, we propose a multilayered deep convolutional neural network. The proposed model works in two blocks: Block-I convolutional neural network (B-I CNN), for detection and occurrence of disasters, and Block-II convolutional neural network (B-II CNN), for classification of natural disaster intensity types with different filters and parameters. The model is tested on 4428 natural images and performance is calculated and expressed as different statistical values: sensitivity (SE), 97.54%; specificity (SP), 98.22%; accuracy rate (AR), 99.92%; precision (PRE), 97.79%; and F1-score (F1), 97.97%. The overall accuracy for the whole model is 99.92%, which is competitive and comparable with state-of-the-art algorithms.

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Detection of Wildfire Smoke Images Based on a Densely Dilated Convolutional Network

Cited by 42 publications

References 34 publications

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

A review of machine learning applications in wildfire science and management

Natural Disasters Intensity Analysis and Classification Based on Multispectral Images Using Multi-Layered Deep Convolutional Neural Network

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